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Zhimin CaoThe Chinese University of Hong Kong Qi YinITCS, Tsinghua University Xiaoou TangShenzhen Institutes of Advanced Technology Chinese Academy of.

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Presentation on theme: "Zhimin CaoThe Chinese University of Hong Kong Qi YinITCS, Tsinghua University Xiaoou TangShenzhen Institutes of Advanced Technology Chinese Academy of."— Presentation transcript:

1 Zhimin CaoThe Chinese University of Hong Kong Qi YinITCS, Tsinghua University Xiaoou TangShenzhen Institutes of Advanced Technology Chinese Academy of Sciences, China Jian SunMicrosoft Research Asia

2 1. Introduction 2. Overview of framework 3. Learning-based descriptor extraction 4. Pose-adaptive matching 5. Experimental results 6. Conclusion and discussion

3  LBP, SIFT or HOG are effective descriptors using handcrafted encoding.  However, existing handcrafted encoding methods suffer two drawbacks:  Manually getting an optimal encoding method is difficult.  Handcrafted codes are usually unevenly distributed distribution of code emergence frequency in 1000 face images

4  learning-based encoding method uses unsupervised learning methods to encode the local microstructures of the face into a set of discrete codes.  Apply PCA and proper normalization mechanism to improve the discriminative ability of the code histogram.  training a set of pose-specific classifiers (each for one specific pose combination) to make the final decision. (1000 face images)

5 “pose-adaptive face matching” pipeline “learning-based descriptor” pipeline

6  Sampling and normalization  sample r*8 neighboring pixels at even intervals on the ring of radius r to form a low-level feature vector.  normalize the sampled feature vector into unit length. (1)R1 = 1, with center; (2)R1 = 1,R2 = 2, with center; (3)R1 = 3, no center; (4)R1 = 4,R2 = 7, no center.

7  Learning-based encoding and histogram representation  three unsupervised learning methods: ▪ K-means ▪ PCA tree ▪ Random-projection tree  encoding method is applied to encode the normalized feature vector into discrete codes and then get local filter response codebook.

8  After the encoding, the input image is turned into a “code” image.  Divide the encoded image into a grid of patches and compute a histogram of the LE codes for each patch. ▪ e.g. 5×7 patches for the holistic face (84×96)  Concatenate all patch histograms to form the descriptor of the whole face image.

9  Select 1,000 images from the LFW training set  LE descriptors start to beat existing descriptors when the code number reaches 32.

10  PCA dimension reduction  resulting face feature may be too large. ▪ e.g. 256 codes × 35 patch = 8,960  400 dimension  normalization is applied after the PCA compression improves the performance.  Multiple LE descriptors  Generally, training a linear SVM to combine the similarity scores generated by different LE descriptors can always achieve better result.

11  choose 256 code and 400 PCA-dimension as our default setting  The recognition rate of PCA with L1 or L2 normalization version can be higher than non PCA and PCA only version.

12  the combination of four LE descriptors obtained the best performance on the LFW.

13  Component-level face alignment  Use 9 face components alignment to replace holistic alignment separately using similarity transform.  face similarity score is the sum of similarities between corresponding components.  more accurately align each component without balancing across the whole face and the negative effect of landmark error will also be reduced

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15  Pose-adaptive matching  each component contributes differently for the recognition when the pose combination of the matching pair is different. ▪ e.g. the right eye is less effective when we match a frontal face and a right-turned face  categorize the pose of the input face to one of three poses (frontal (F), left (L), and right (R)).  Select three gallery images from the Multi-PIE dataset and measure the similarity between the probe face and them.  pose label of the most alike gallery image is assigned to the probe face.

16  pose combinations of a face pair could be {FF, LL, RR, LR (RL), LF (FL), RF (FR)}.  each by a subset of training pairs with a specific pose combination trained a linear SVM classifier by a subset of training pairs.  final pose-adaptive classifier consists of 6 linear SVM classifiers.  The “best-fit” classifier having the same pose combination with the input matching pair makes the final decision.

17  Randomly sampling 3,000 intra-/extra-personal pairs from LFW for each pose combination. ▪ e.g. pair number is 3, 000 × 6 = 18, 000 Before: % ±0.41 % After: % ±0.42 %

18  Results on the LFW benchmark

19  Results on the Multi-PIE  The default descriptors trained on the LFW benchmark are adopted in the experiments.  randomly generate 10 subsets of face images with Multi-PIE, each has 300 intra-personal and 300 extra-personal image pairs.

20  face recognition using learning-based (LE) descriptor and pose-adaptive matching do well on the LFW benchmark.  excellent generalization ability on Multi-PIE.  Replace manually designed pattern sampling by automating may produce a more powerful descriptor for face recognition.


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