http://wiki.math.uwaterloo.ca/statwiki/index.php?title=video-based_face_recognition_using_Adaptive_HMM&feed=atom&action=historyvideo-based face recognition using Adaptive HMM - Revision history2024-03-28T16:23:39ZRevision history for this page on the wikiMediaWiki 1.41.0http://wiki.math.uwaterloo.ca/statwiki/index.php?title=video-based_face_recognition_using_Adaptive_HMM&diff=27602&oldid=prevConversion script: Conversion script moved page Video-based face recognition using Adaptive HMM to video-based face recognition using Adaptive HMM: Converting page titles to lowercase2017-08-30T13:46:01Z<p>Conversion script moved page <a href="/statwiki/index.php?title=Video-based_face_recognition_using_Adaptive_HMM" class="mw-redirect" title="Video-based face recognition using Adaptive HMM">Video-based face recognition using Adaptive HMM</a> to <a href="/statwiki/index.php?title=video-based_face_recognition_using_Adaptive_HMM" title="video-based face recognition using Adaptive HMM">video-based face recognition using Adaptive HMM</a>: Converting page titles to lowercase</p>
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</td></tr></table>Conversion scripthttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=video-based_face_recognition_using_Adaptive_HMM&diff=15407&oldid=prevVsmanem: /* Adaptive HMM */2011-11-29T20:09:10Z<p><span dir="auto"><span class="autocomment">Adaptive HMM</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Adaptive HMM=</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Adaptive HMM=</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Motivated by speech speaker-dependent recognition this paper proposed an adaptive HMM that trains the HMM during the recognition process. At the recognition process step; after a test sequence is recognized for one subject;, this same sequence is used to update the HMM of that subject. <del style="font-weight: bold; text-decoration: none;">The </del>adaptive learning approach has some challenges as there is a need to estimate the correctness and the value of the new information. The proposed model in this study computes the likelihood difference between the estimated likelihood and a predefined threshold determine through experiments. Then the model uses EM algorithm to iteratively estimate the Maximum a posterior MAP which used to adapt the HMM given initial state <math>\lambda_{old}</math>,and observation vectors O. We should mention that the covariance is not updated but the mean is updated as follow.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Motivated by speech speaker-dependent recognition this paper proposed an adaptive HMM that trains the HMM during the recognition process. At the recognition process step; after a test sequence is recognized for one subject;, this same sequence is used to update the HMM of that subject. <ins style="font-weight: bold; text-decoration: none;">Two questions have to be addressed in this scenario. Firstly, the basis on which we justify the current sequence to be used as an updating sequence for its successive iterations, and secondly using HMM. Hence, this </ins>adaptive learning approach has some challenges as there is a need to estimate the correctness and the value of the new information. The proposed model in this study computes the likelihood difference between the estimated likelihood and a predefined threshold determine through experiments. Then the model uses EM algorithm to iteratively estimate the Maximum a posterior MAP which used to adapt the HMM given initial state <math>\lambda_{old}</math>,and observation vectors O. We should mention that the covariance is not updated but the mean is updated as follow.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>\mu_{ik}=(1-\beta)\mu^{old}_{ik}+\beta\frac {\sum_{t=1}^T O_t P(q_{t}=i, m_{qt}=k|O,\lambda)}{\sum_{t=1}^T P(q_{t}=i,m_{qt}=k|O,\lambda)}</math></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>\mu_{ik}=(1-\beta)\mu^{old}_{ik}+\beta\frac {\sum_{t=1}^T O_t P(q_{t}=i, m_{qt}=k|O,\lambda)}{\sum_{t=1}^T P(q_{t}=i,m_{qt}=k|O,\lambda)}</math></div></td></tr>
</table>Vsmanemhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=video-based_face_recognition_using_Adaptive_HMM&diff=15395&oldid=prevMazen.Melibari: /* Features extraction */2011-11-29T16:55:43Z<p><span dir="auto"><span class="autocomment">Features extraction</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><math>F_l</math> is a tuple of T training face images of subject l. <del style="font-weight: bold; text-decoration: none;">Each image </del>in this dataset only contains the face of the <del style="font-weight: bold; text-decoration: none;">subject</del>. Several eigenvectors, <math>{V_1, V_2, ..., V_d}</math>, obtained by performing eigen-analysis on the L*T training samples. Corresponding feature vector of each image, <math>e_{l,t}</math>, is then generated by projecting the training images into the obtained eigenvectors. The set of all the projected training images (feature vectors) is then used as observations to train the HMM.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><math>F_l</math> is a tuple of T training face images of subject l. <ins style="font-weight: bold; text-decoration: none;">The images </ins>in this dataset only contains the face <ins style="font-weight: bold; text-decoration: none;">portion </ins>of the <ins style="font-weight: bold; text-decoration: none;">subjects</ins>. Several eigenvectors, <math>{V_1, V_2, ..., V_d}</math>, obtained by performing eigen-analysis on the L*T training samples. Corresponding feature vector of each image, <math>e_{l,t}</math>, is then generated by projecting the training images into the obtained eigenvectors. The set of all the projected training images (feature vectors) is then used as observations to train the HMM.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Temporal HMM=</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Temporal HMM=</div></td></tr>
</table>Mazen.Melibarihttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=video-based_face_recognition_using_Adaptive_HMM&diff=15394&oldid=prevMazen.Melibari: /* Features extraction */2011-11-29T16:55:09Z<p><span dir="auto"><span class="autocomment">Features extraction</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>\, 1 \leq l \leq L</math></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>\, 1 \leq l \leq L</math></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><math>F_l</math> <del style="font-weight: bold; text-decoration: none;">here </del>is a tuple of T training face images of subject l. Each image in this dataset only contains the face of the subject. Several eigenvectors, <math>{V_1, V_2, ..., V_d}</math>, obtained by performing eigen-analysis on the L*T training samples. Corresponding feature vector of each image, <math>e_{l,t}</math>, is then generated by projecting the training images into the obtained eigenvectors. The set of all the projected training images (feature vectors) is then used as observations to train the HMM.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><math>F_l</math> is a tuple of T training face images of subject l. Each image in this dataset only contains the face of the subject. Several eigenvectors, <math>{V_1, V_2, ..., V_d}</math>, obtained by performing eigen-analysis on the L*T training samples. Corresponding feature vector of each image, <math>e_{l,t}</math>, is then generated by projecting the training images into the obtained eigenvectors. The set of all the projected training images (feature vectors) is then used as observations to train the HMM.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Temporal HMM=</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Temporal HMM=</div></td></tr>
</table>Mazen.Melibarihttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=video-based_face_recognition_using_Adaptive_HMM&diff=15393&oldid=prevMazen.Melibari: /* Features extraction */2011-11-29T16:54:17Z<p><span dir="auto"><span class="autocomment">Features extraction</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>\, 1 \leq l \leq L</math></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>\, 1 \leq l \leq L</math></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><math>F_l</math> here is a tuple of T training face images of subject l.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><math>F_l</math> here is a tuple of T training face images of subject l. <ins style="font-weight: bold; text-decoration: none;">Each image in this dataset only contains the face </ins>of <ins style="font-weight: bold; text-decoration: none;">the subject. Several eigenvectors, </ins><math>{<ins style="font-weight: bold; text-decoration: none;">V_1, V_2, ...</ins>, <ins style="font-weight: bold; text-decoration: none;">V_d</ins>}</math><ins style="font-weight: bold; text-decoration: none;">, obtained by performing eigen-analysis on the L*T training samples</ins>. <ins style="font-weight: bold; text-decoration: none;">Corresponding feature </ins>vector <ins style="font-weight: bold; text-decoration: none;">of each image, </ins><math>e_{l,t}</math><ins style="font-weight: bold; text-decoration: none;">, is then generated by projecting the training images into the obtained eigenvectors. The set of all the projected training images (feature vectors) is then used as observations to train the HMM.</ins></div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">applied on each on T number </del>of <del style="font-weight: bold; text-decoration: none;">images for each L subjects to generates corresponding feature vectors </del><math><del style="font-weight: bold; text-decoration: none;">e_</del>{<del style="font-weight: bold; text-decoration: none;">l</del>,<del style="font-weight: bold; text-decoration: none;">t</del>}</math>. <del style="font-weight: bold; text-decoration: none;">For all the features vectors the mean </del>vector <del style="font-weight: bold; text-decoration: none;"><math>\mu</math> and the covariance matrix <math>C_e</math> were computed. </del></div></td><td colspan="2" class="diff-side-added"></td></tr>
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<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"><math>O_l</math>={</del><math>e_<del style="font-weight: bold; text-decoration: none;">{l,1},e_{1,2},e_{l,3},……e_</del>{l,t}</math><del style="font-weight: bold; text-decoration: none;">}</del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Temporal HMM=</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Temporal HMM=</div></td></tr>
</table>Mazen.Melibarihttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=video-based_face_recognition_using_Adaptive_HMM&diff=15392&oldid=prevMazen.Melibari: /* Features extraction */2011-11-29T16:44:48Z<p><span dir="auto"><span class="autocomment">Features extraction</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Features extraction=</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Features extraction=</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>In computer vision there are common approaches that are used for feature extraction such as Pixel value ,Eigen-coefficients,and DCT. These approaches help us to reduce the dimensionality and solve the problem in feature space. Without doing this step our problem will be computationally intractable. In this study Principal Component Analysis PCA was used to represent the images in low-dimensional features. The Eigenanalysis was performed to produce new features vectors projected in the eigenspace by computing the covariance, eigenvectors and eigenvalues. The Feature extraction procedure was applied on each on T number of images for each L subjects to generates corresponding feature vectors <math>e_{l,t}</math>. For all the features vectors the mean vector <math>\mu</math> and the covariance matrix <math>C_e</math> were computed. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>In computer vision there are common approaches that are used for feature extraction such as Pixel value ,Eigen-coefficients,and DCT. These approaches help us to reduce the dimensionality and solve the problem in feature space. Without doing this step our problem will be computationally intractable. In this study Principal Component Analysis PCA was used to represent the images in low-dimensional features. The Eigenanalysis was performed to produce new features vectors projected in the eigenspace by computing the covariance, eigenvectors and eigenvalues. The Feature extraction procedure was <ins style="font-weight: bold; text-decoration: none;">as follows. The given face database contains T number of images for each subject, where we have a total of L subjects:</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><math>\, F_l = \{ f_{l,1},f_{1,2},f_{l,3},……f_{l,t} \} </math></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><math>\, 1 \leq l \leq L</math></ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><math>F_l</math> here is a tuple of T training face images of subject l.</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>applied on each on T number of images for each L subjects to generates corresponding feature vectors <math>e_{l,t}</math>. For all the features vectors the mean vector <math>\mu</math> and the covariance matrix <math>C_e</math> were computed. </div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;"><math>F_l</math>={<math>f_{l,1},f_{1,2},f_{l,3},……f_{l,t}</math>}</del></div></td><td colspan="2" class="diff-side-added"></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>O_l</math>={<math>e_{l,1},e_{1,2},e_{l,3},……e_{l,t}</math>}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>O_l</math>={<math>e_{l,1},e_{1,2},e_{l,3},……e_{l,t}</math>}</div></td></tr>
</table>Mazen.Melibarihttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=video-based_face_recognition_using_Adaptive_HMM&diff=15388&oldid=prevM2rostam: /* Features extraction */2011-11-29T04:09:48Z<p><span dir="auto"><span class="autocomment">Features extraction</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 00:09, 29 November 2011</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Features extraction=</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Features extraction=</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>In computer vision there are common approaches that are used for feature extraction such as Pixel value ,Eigen-coefficients,and DCT. These approaches help us to reduce the dimensionality and solve the problem in feature space. In this study Principal Component Analysis PCA <del style="font-weight: bold; text-decoration: none;">were </del>used to represent the images in low-dimensional features. The Eigenanalysis was performed to produce new features vectors projected in the eigenspace by computing the covariance, eigenvectors and eigenvalues. The Feature extraction procedure was applied on each on T number of images for each L subjects to generates corresponding feature vectors <math>e_{l,t}</math>. For all the features vectors the mean vector <math>\mu</math> and the covariance matrix <math>C_e</math> were computed. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>In computer vision there are common approaches that are used for feature extraction such as Pixel value ,Eigen-coefficients,and DCT. These approaches help us to reduce the dimensionality and solve the problem in feature space<ins style="font-weight: bold; text-decoration: none;">. Without doing this step our problem will be computationally intractable</ins>. In this study Principal Component Analysis PCA <ins style="font-weight: bold; text-decoration: none;">was </ins>used to represent the images in low-dimensional features. The Eigenanalysis was performed to produce new features vectors projected in the eigenspace by computing the covariance, eigenvectors and eigenvalues. The Feature extraction procedure was applied on each on T number of images for each L subjects to generates corresponding feature vectors <math>e_{l,t}</math>. For all the features vectors the mean vector <math>\mu</math> and the covariance matrix <math>C_e</math> were computed. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>F_l</math>={<math>f_{l,1},f_{1,2},f_{l,3},……f_{l,t}</math>}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>F_l</math>={<math>f_{l,1},f_{1,2},f_{l,3},……f_{l,t}</math>}</div></td></tr>
</table>M2rostamhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=video-based_face_recognition_using_Adaptive_HMM&diff=15387&oldid=prevM2rostam: /* Conclusion */2011-11-29T04:06:52Z<p><span dir="auto"><span class="autocomment">Conclusion</span></span></p>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Conclusion=</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Conclusion=</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The adaptive model in this paper train the HMM on each subjects video sequence and temporal information. Then during the recognition process, the model estimate the likelihood of HMMS using the test data and the highest score will be the identity of the test video. The proposed model evaluated with image-based methods and found to provide better results; however, this could be attributed to different reasons rather than using video-based features or adaptive approach such as using Gaussian mixture model to computed the probability density distribution. One of the future directions is to combine the spatial HMM with the temporal HMM of video sequences; which might pave a way to model both face recognition and face tracking in one model.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The adaptive model in this paper train the HMM on each subjects video sequence and temporal information. Then during the recognition process, the model estimate the likelihood of HMMS using the test data and the highest score will be the identity of the test video. The proposed model evaluated with image-based methods and found to provide better results; however, this could be attributed to different reasons rather than using video-based features or adaptive approach such as using Gaussian mixture model to computed the probability density distribution. One of the future directions is to combine the spatial HMM with the temporal HMM of video sequences; which might pave a way to model both face recognition and face tracking in one model.</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">{{Cleanup|date=November 2011|reason= It is worth to add experiential results and comparisons and to mention the superiority of this approach over other alternatives.}}</ins></div></td></tr>
</table>M2rostamhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=video-based_face_recognition_using_Adaptive_HMM&diff=15386&oldid=prevM2rostam: /* Human face recognition */2011-11-29T03:48:10Z<p><span dir="auto"><span class="autocomment">Human face recognition</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 23:48, 28 November 2011</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Introduction =</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Introduction =</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==Human face recognition==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>==Human face recognition==</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Human face recognition is a subarea of object recognition which aims to identify a face given a scene or still images. Face recognition benefits many fields such as computer security and video compression. Two approaches are commonly used in face recognition are video-based and still images. Since the 80's, image-based recognition is more dominant in face recognition in comparison with the video-based approach. Few recent studies took advantages of the features of video scenes as it provides more dynamic characteristic of the human face that help the recognition process. Also, farm sequences provide more features of 3D representation and high resolution images. Besides, in video-based recognition the prediction accuracy can be improved using the farm sequence. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Human face recognition is a subarea of object recognition which aims to identify a face given a scene or still images. <ins style="font-weight: bold; text-decoration: none;">It is very complex problem with high dimensionality due to the nature of digital images. </ins>Face recognition benefits many fields such as computer security and video compression. Two approaches are commonly used in face recognition are video-based and still images. Since the 80's, image-based recognition is more dominant in face recognition in comparison with the video-based approach. Few recent studies took advantages of the features of video scenes as it provides more dynamic characteristic of the human face that help the recognition process. Also, farm sequences provide more features of 3D representation and high resolution images. Besides, in video-based recognition the prediction accuracy can be improved using the farm sequence. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:TemporalHMM.png|thumb|right|Fig.1 Temporal HMM graph.]]</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[File:TemporalHMM.png|thumb|right|Fig.1 Temporal HMM graph.]]</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Motivated by speaker adaptation, this paper presents an Adaptive Hidden Markov model to recognize human face from frames sequence. The proposed model trains HMM on the training data and then improves the recognition constantly using the test data. A sample figure is displayed in Figure 1 that captures the following:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Motivated by speaker adaptation, this paper presents an Adaptive Hidden Markov model to recognize human face from frames sequence. The proposed model trains HMM on the training data and then improves the recognition constantly using the test data. A sample figure is displayed in Figure 1 that captures the following:</div></td></tr>
</table>M2rostamhttp://wiki.math.uwaterloo.ca/statwiki/index.php?title=video-based_face_recognition_using_Adaptive_HMM&diff=15384&oldid=prevM2rostam: /* Features extraction */2011-11-29T03:46:52Z<p><span dir="auto"><span class="autocomment">Features extraction</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 23:46, 28 November 2011</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Features extraction=</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>=Features extraction=</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>In computer vision there <del style="font-weight: bold; text-decoration: none;">is </del>common approaches that <del style="font-weight: bold; text-decoration: none;">in </del>used for <del style="font-weight: bold; text-decoration: none;">features </del>extraction such as Pixel value ,Eigen-coefficients,and DCT. In this study Principal Component Analysis PCA were used to represent the images in low-dimensional features. The Eigenanalysis was performed to produce new features vectors projected in the eigenspace by computing the covariance, eigenvectors and eigenvalues. The Feature extraction procedure was applied on each on T number of images for each L subjects to generates corresponding feature vectors <math>e_{l,t}</math>. For all the features vectors the mean vector <math>\mu</math> and the covariance matrix <math>C_e</math> were computed. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>In computer vision there <ins style="font-weight: bold; text-decoration: none;">are </ins>common approaches that <ins style="font-weight: bold; text-decoration: none;">are </ins>used for <ins style="font-weight: bold; text-decoration: none;">feature </ins>extraction such as Pixel value ,Eigen-coefficients,and DCT<ins style="font-weight: bold; text-decoration: none;">. These approaches help us to reduce the dimensionality and solve the problem in feature space</ins>. In this study Principal Component Analysis PCA were used to represent the images in low-dimensional features. The Eigenanalysis was performed to produce new features vectors projected in the eigenspace by computing the covariance, eigenvectors and eigenvalues. The Feature extraction procedure was applied on each on T number of images for each L subjects to generates corresponding feature vectors <math>e_{l,t}</math>. For all the features vectors the mean vector <math>\mu</math> and the covariance matrix <math>C_e</math> were computed. </div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>F_l</math>={<math>f_{l,1},f_{1,2},f_{l,3},……f_{l,t}</math>}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><math>F_l</math>={<math>f_{l,1},f_{1,2},f_{l,3},……f_{l,t}</math>}</div></td></tr>
</table>M2rostam