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Dictionary-based Face Recognition from Video ECCV 2012

Dictionary-based Face Recognition from Video ECCV 2012. Yuanhao Guo. I Problems. Most FR systems are still face images recognition. Different video sequences contain variations in resolution, illumination, pose, and facial expressions— Challenging !

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Dictionary-based Face Recognition from Video ECCV 2012

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  1. Dictionary-based Face Recognition from VideoECCV 2012 Yuanhao Guo

  2. I Problems • Most FR systems are still face images recognition. • Different video sequences contain variations in resolution, illumination, pose, and facial expressions—Challenging! • Numerous video-based FR methods are ‘multi-still face recognition’ – regard each frame as a still image. • Working on the FOCS video database (146 objects, 770 videos)

  3. II Proposed Method • Training: • step 1: crop face

  4. II Proposed Method • Training: step 2: K-means clustering (different pose and illumination) K ?

  5. II Proposed Method • Training: step 3: Building Sequence-specific Dictionaries • is the j-th partition of object i in k-th video. j-thdictionary of subject i

  6. II Proposed Method 2. Testing: Given some videos , means the m-th video, the k-th partition. is the l-thimage.

  7. II Proposed Method 2. Testing: Finally, using the knowledge of the correspondence m(p*) between subjects and sequences, we assign the query video sequence Q(m) to subject i* = m(p*)

  8. II Conclusions 1. Partition the multiple frames from a video into K clusters according to different pose and lightening. First of all, remove the effects of noise. Second, reduce the testing complexity 2. However, if the training videos number increases, such as 10,000 of 1,000 objects, how does this method perform? 3. Very simple question: how are the cropped faces aligned to same size? 4. More poses and lightening variations produce more clusters?

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