Face Recognition Based on Deep Learning (Yurii Pashchenko Technology Stream)
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Face Recognition Based on Deep Learning (Yurii Pashchenko Technology Stream)
- 1. Face Recognition Based on Deep Learning recognizzit www.recognizz.it
- 3. Benchmarks ORL FERET Labeled Faces in the Wild (LFW) YouTube Faces (YTF)
- 4. ORL Images taken between April 1992 and April 1994 at the lab There are 10 different images of each of 40 distinct subjects The size of each image is 92x112 pixels, with 256 grey levels per pixel ~10 mb * http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
- 5. FERET Images taken in 2 steps: 1993-1997 1998-2001 1200 subjects, 14051 face images fa fb dublicate 1 fc dublicate 2 Heads with views ranging from frontal to left and right profiles Different lightning Size ~ 8GB * http://www.itl.nist.gov/iad/humanid/feret/feret_master.html
- 6. Labeled Faces in the Wild Faces collected from the web 1680 subjects, 13 000 face images Size ~200mb * http://vis-www.cs.umass.edu/lfw/
- 7. LFW unrestricted with labeled outside data protocol 10 batches Each batch: 300 matched 300 mismatched Huang, G.B., Learned-Miller, E.: Labeled faces in the wild: Updates and new reporting procedures. Technical Report UM-CS-2014-003, UMass Amherst (2014) Round 1 Validation Set Training Set Round 2 Round 3 Round 10 ...
- 8. Face Recognition Algorithm Face localization Normalization Feature extraction Comparing Verification metric FALSE
- 9. What was before DCN Method Accuracy ± SE combined Joint Bayesian 0.9242 ±0.0108 Tom-vs-Pete + Attribute 0.9330 ±0.0128 High-dim LBP 0.9517 ±0.0113 TL Joint Bayesian 0.9633 ± 0.0108 Human, cropped 0.975
- 10. DeepFace Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face verification. In CVPR, 2014
- 11. Training data Dataset: Social Face Classification (4030 subjects, ~4.4 million face images) Alignment: 2D 3D
- 12. Architecture Input: RGB image 152x152 Output feature size: 4096 Parameters: ~ 120 million Verification metric: weighted chi-squared distance siamese
- 13. Results on LFW Method Accuracy ± SE combined Joint Bayesian 0.9242 ±0.0108 Tom-vs-Pete + Attribute 0.9330 ±0.0128 High-dim LBP 0.9517 ±0.0113 TL Joint Bayesian 0.9633 ± 0.0108 DeepFace-align2D 0.943 ± 0.0043 DeepFace-Siamese 0.9617 ± 0.0038 DeepFace-ensemble 0.9735 ± 0.0025 Human, cropped 0.975
- 14. DeepID Y. Sun, X. Wang, and X. Tang. Deep learning face representation from predicting 10,000 classes. In CVPR, 2014.
- 15. Training data Dataset: CelebFaces (10 177 subjects, 202 599 face images) Alignment: 2D Patch
- 16. Architecture Input: 39x31 RGB or grayscale Output feature size: 160 Additional algorithms: Joint Bayesian PCAONLY 1 PATCH
- 17. Results on LFW Method Accuracy ± SE#Net DeepFace-Siamese 1 7 60 100 100 0.9617 ± 0.0038 DeepFace-ensemble 0.9735 ± 0.0025 DeepID on CelebFaces 0.9605 ± … DeepID on CelebFaces+ 0.972 ± … DeepID on CelebFaces+ TL 0.9745 ± 0.0026 Human, cropped 0.975
- 18. CASIA D. Yi, Z. Lei, S. Liao, and S. Z. Li. Learning face representation from scratch. Technical report, arXiv:1411.7923, 2014.
- 19. Training data Dataset: CASIA WebFace (10 575 subjects and 494 414 face images) Alignment: 2D
- 20. Architecture Input: gray image 100x100 Output feature vector size: 320 Parameters:~ 5 million Verification metric: Cosine Additional algorithms: Joint Bayes PCA Combined identification and verification loss Very deep architecture Face Labels Cost2 Contrastive Verification Labels [0,1] Cost1 Softmax Conv12 Conv11 Poo11 2x2+2(S) Conv22 Conv21 Poo12 2x2+2(S) Conv32 Conv31 Poo13 2x2+2(S) Fc6 Dropcut 40% Poo15 7x7+1(S) Conv51 Conv52 Poo14 2x2+2(S) Conv41 Conv42
- 21. Results on LFW Method Accuracy ± SE#Net DeepFace 7 100 1 1 1 1 1 0.9735 ± 0.0025 DeepID 0.9745 ± 0.0026 DR + Cosine 0.9613 ± 0.003 DR + PCA on CASIA-WebFace + Cosine 0.963 ± 0.0035 DR + Joint Bayes on CASIA-WebFace 0.973 ± 0.0031 DR + PCA on LFW training set + Cosine DR + Joint Bayse on LFW training set 0.9633 ± 0.0042 0.9773 ± 0.0031 * DR – CASIA-WebFace
- 22. CASIA replication Training data Accuracy on LFW normalized 97,27 % no alignment 94 % our alignment 96,2 %
- 23. Database size Jingtuo Liu, Yafeng Deng, Tao Bai, Zhengping Wei, and Chang Huang; Baidu�Targeting Ultimate Accuracy: Face Recognition via Deep Embedding. Identities Error rate 1.5K 3.1% 9K 1.35% 18K Faces 150K 450K 1.2M 0.87%
- 24. Database availability Dataset #Images#Subjects LFW 5 749 2 995 10 177 4 030 2 000 10 575 13 233 WDRef 99 773 CelebFaces 202 599 SFC 4 400 000 CACD 163 446 CASIA-WebFace 494 414 Availability Public Public (feature only) Private Private Public (partial annotated) Public D. Yi, Z. Lei, S. Liao, and S. Z. Li. Learning face representation from scratch. Technical report, arXiv:1411.7923, 2014.
- 25. Recommendations Big dataset with large amount of subjects Careful face extraction and alignment Deep architecture Joint Identification-Verification K. Simonyan and A. Zisserman. “Very deep convolutional networks for large-scale image recognition”. arXiv preprint arXiv:1409.1556, 2014 Y. Sun, Y. Chen, X. Wang, and X. Tang. Deep learning face representation by joint identification-verification. In NIPS, 2014.