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PR 127: FaceNet

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Taeoh Kim

The document discusses the FaceNet paper, which proposes a unified embedding for face recognition and clustering using a deep neural network. Some key points: - FaceNet uses a triplet loss during training to learn a embedding space where distances between faces correspond to whether they are from the same person or not. - This eliminates the need for complex multi-stage training pipelines used by previous works. - On standard benchmarks, FaceNet achieves over 99% accuracy for face verification, outperforming prior state-of-the-art models. - The unified embedding allows for face recognition via distance thresholding and face clustering via k-means in the learned space.

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FaceNet: A Unified Embedding for Face Recognition and Clustering PR-127 PR12 Season 2 Taeoh Kim, Tensorflow-KR Image/Video Pattern Recognition Lab School of Electrical & Electronic Engineering
References Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet • Deep Face Recognition: A Survey, M. Wang et al. • FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR 2015, Google
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Background 1: FR System Figure from Deep Face Recognition: A Survey Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Background 1: FR System: Today Figure from Deep Face Recognition: A Survey Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Background 1: FR History Figure from Deep Face Recognition: A Survey Paper Citations >1500 >500 >100
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Background 1: FR History Figure from Deep Face Recognition: A Survey Paper Today Maybe Next..?
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Background 2: FR Approach The Gallery: Target IDs The Proble: Test ID Identification: One-to-many, ID? Verification: One-to-one, Yes/No? * Training using Identification Loss Fine-tuning using Id/Verification Loss and Test Figure from SphereFace Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Background 3: DFR History Figure from Deep Face Recognition: A Survey Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Deep Learning Breakthrough Figures from DFD & DeepFace Papers Hand-crafted Learned Filter based 84.02% Acc. TPAMI 2014 AlexNet based 97.35% Acc. CVPR 2014
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepFace (by Facebook, CVPR 2014) Figure from DeepFace Paper • Training with Cross-Entropy Softmax Loss (Classification Loss) • Fine-tune a Feature Representation using Chi-square Distance / Siamese Net 𝜒2 𝑓1, 𝑓2 = ෍ 𝑖 𝑤𝑖 𝑓1 𝑖 − 𝑓2 𝑖 2 (𝑓1 𝑖 + 𝑓2[𝑖]) 𝑦 = 𝜎(𝜒2 𝑓1, 𝑓2 )
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Ref) Siamese Network Figure from Andrew Ng’s Course
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepFace (by Facebook, CVPR 2014)
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepID2 (by HKU, NIPS 2014)
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Figure from Deep Face Recognition: A Survey Paper Citations >1500 >500 >100
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepID2 (by HKU, NIPS 2014) 𝐿 = 𝑆𝑜𝑓𝑡𝑚𝑎𝑥 + 𝑉𝑒𝑟𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑉𝑒𝑟𝑖𝑓 = 1 2 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝑆𝑎𝑚𝑒 𝐼𝐷 1 2 max 0, 𝑚 − 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝐷𝑖𝑓𝑓. 𝐼𝐷 Figure from DeepID2 Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepID2 (by HKU, NIPS 2014) 𝐿 = 𝑆𝑜𝑓𝑡𝑚𝑎𝑥 + 𝑉𝑒𝑟𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑉𝑒𝑟𝑖𝑓 = 1 2 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝑆𝑎𝑚𝑒 𝐼𝐷 1 2 max 0, 𝑚 − 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝐷𝑖𝑓𝑓. 𝐼𝐷 Euclidean Space Figure from DeepID2 Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepID2 (by HKU, NIPS 2014) 𝐿 = 𝑆𝑜𝑓𝑡𝑚𝑎𝑥 + 𝑉𝑒𝑟𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑉𝑒𝑟𝑖𝑓 = 1 2 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝑆𝑎𝑚𝑒 𝐼𝐷 1 2 max 0, 𝑚 − 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝐷𝑖𝑓𝑓. 𝐼𝐷 Compresses Intra-ID Variance Figure from DeepID2 Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepID2 (by HKU, NIPS 2014) 𝐿 = 𝑆𝑜𝑓𝑡𝑚𝑎𝑥 + 𝑉𝑒𝑟𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑉𝑒𝑟𝑖𝑓 = 1 2 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝑆𝑎𝑚𝑒 𝐼𝐷 1 2 max 0, 𝑚 − 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝐷𝑖𝑓𝑓. 𝐼𝐷 Enlarges Inter-ID Variance Figure from DeepID2 Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepID2 (by HKU, NIPS 2014) • Fine-tune for Verification using Joint Bayesian (98.97  99.15%) Table from DeepID2 Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Figure from Deep Face Recognition: A Survey Paper Citations >1500 >500 >100
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet (by Google, CVPR 2015)
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet (by Google, CVPR 2015) Table from LFW Survey Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet (by Google, CVPR 2015) Before FaceNet - Training using Identification Loss (+ Contrastive Loss) - Fine Tune (using Metric Learning / Joint Bayesian) FaceNet - Training using Metric Learning
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Metric Learning
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet (by Google, CVPR 2015) • Very Big Data (~260M) • Very Deep Network (ZFNet, GoogleNet) • No Face Alignment • Single Model • Verification Loss (Metric) Only: Face L2 Embedding - Verification: Thresholding the Distance between the two embeddings - Identification: k-NN Classification - Clustering: k-means Clustering
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Verification = Thresholding
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Identifiaction = K-NN Classification
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Clustering = K-means
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet (by Google, CVPR 2015) • Very Big Data (~260M) • Very Deep Network (ZFNet, GoogleNet) • No Face Alignment • Single Model • Verification Loss (Metric) Only: Face L2 Embedding - Verification: Thresholding the Distance between the two embeddings - Identification: k-NN Classification - Clustering: k-means Clustering Triplet Loss
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet (by Google, CVPR 2015) Figure from FaceNet Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Triplet Loss 𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑝 2 2 Anchor Positive Figures from LFW Dataset
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Triplet Loss 𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝛼 − 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑛 2 2 Anchor Negative Figures from LFW Dataset
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Triplet Loss 𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑝 2 2 − 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑛 2 2 + 𝛼 + For all Triplets Anchor Negative Positive Figures from LFW Dataset
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Triplet Loss Training Issue • Large batch size for Anchor-Positive-Negative Balance • Hard Positive in Mini Batch 𝑎𝑟𝑔𝑚𝑎𝑥 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑝 2 2 • Hard Negative in Mini-Batch 𝑎𝑟𝑔𝑚𝑖𝑛 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑛 2 2 Figure from FaceNet Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Triplet Loss Training Issue • Large batch size for Anchor-Positive-Negative Balance • Hard Positive in Mini Batch 𝑎𝑟𝑔𝑚𝑎𝑥 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑝 2 2  All Anchor-Positive • Hard Negative in Mini-Batch 𝑎𝑟𝑔𝑚𝑖𝑛 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑛 2 2  Semi-hard 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑝 2 2 < 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑛 2 2 Figure from FaceNet Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet: Results Figure from FaceNet Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Figure from Deep Face Recognition: A Survey Paper Citations >1500 >500 >100
VGGFace (by Oxford, BMVC 2015) Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet
VGGFace (by Oxford, BMVC 2015) Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Method Images Networks Acc. DeepFace 4M 3 97.35 DeepID3 200 99.47 FaceNet 200M 1 99.63 VGGFace 2.6M 1 98.95 Method Images Networks Acc. DeepFace 4M 3 91.4 DeepID3 200 93.2 FaceNet 200M 1 95.1 VGGFace 2.6M 1 97.3 • VGGFace Dataset (Public Available) • Softmax Loss + Triplet Loss • Compact, SOTA Performance LFW Test Perforamance YTF Test Perforamance Figure from VGGFace Paper
Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Figure from Deep Face Recognition: A Survey Paper Citations >1500 >500 >100
Discussion / Practical Issues Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet • In FR, Dataset & Pre-processing is Very Very… Important • Metric Learning vs Classification Loss depends on Applications • Open-set / Closed-set • Identification (Facebook) / Verification (Security, Face ID) • Normal FR Research is Saturated
Face Recognition vs Object Recognition Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet • Small Inter-class Variations / Large Intra-class Variations (Pose, Emotion, Age) • Small Discriminant Features / Low Resolution / Hard Occlusions • Different Scenarios
Metric Learning in Computer Vision = One-shot Learning Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet • Face Recognition • Image Retrieval • Person Re-Identification • Scene Recognition • Object Tracking
FR Research Issues Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Figure from Deep Face Recognition: A Survey Paper
References https://github.com/davidsandberg/facenet Tf implementation of FaceNet Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet
Thank You! Image/Video Pattern Recognition Lab School of Electrical & Electronic Engineering

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PR 127: FaceNet

  • 1. FaceNet: A Unified Embedding for Face Recognition and Clustering PR-127 PR12 Season 2 Taeoh Kim, Tensorflow-KR Image/Video Pattern Recognition Lab School of Electrical & Electronic Engineering
  • 2. References Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet • Deep Face Recognition: A Survey, M. Wang et al. • FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR 2015, Google
  • 3. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Background 1: FR System Figure from Deep Face Recognition: A Survey Paper
  • 4. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Background 1: FR System: Today Figure from Deep Face Recognition: A Survey Paper
  • 5. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Background 1: FR History Figure from Deep Face Recognition: A Survey Paper Citations >1500 >500 >100
  • 6. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Background 1: FR History Figure from Deep Face Recognition: A Survey Paper Today Maybe Next..?
  • 7. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Background 2: FR Approach The Gallery: Target IDs The Proble: Test ID Identification: One-to-many, ID? Verification: One-to-one, Yes/No? * Training using Identification Loss Fine-tuning using Id/Verification Loss and Test Figure from SphereFace Paper
  • 8. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Background 3: DFR History Figure from Deep Face Recognition: A Survey Paper
  • 9. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Deep Learning Breakthrough Figures from DFD & DeepFace Papers Hand-crafted Learned Filter based 84.02% Acc. TPAMI 2014 AlexNet based 97.35% Acc. CVPR 2014
  • 10. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepFace (by Facebook, CVPR 2014) Figure from DeepFace Paper • Training with Cross-Entropy Softmax Loss (Classification Loss) • Fine-tune a Feature Representation using Chi-square Distance / Siamese Net 𝜒2 𝑓1, 𝑓2 = ෍ 𝑖 𝑤𝑖 𝑓1 𝑖 − 𝑓2 𝑖 2 (𝑓1 𝑖 + 𝑓2[𝑖]) 𝑦 = 𝜎(𝜒2 𝑓1, 𝑓2 )
  • 11. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Ref) Siamese Network Figure from Andrew Ng’s Course
  • 12. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepFace (by Facebook, CVPR 2014)
  • 13. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepID2 (by HKU, NIPS 2014)
  • 14. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Figure from Deep Face Recognition: A Survey Paper Citations >1500 >500 >100
  • 15. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepID2 (by HKU, NIPS 2014) 𝐿 = 𝑆𝑜𝑓𝑡𝑚𝑎𝑥 + 𝑉𝑒𝑟𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑉𝑒𝑟𝑖𝑓 = 1 2 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝑆𝑎𝑚𝑒 𝐼𝐷 1 2 max 0, 𝑚 − 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝐷𝑖𝑓𝑓. 𝐼𝐷 Figure from DeepID2 Paper
  • 16. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepID2 (by HKU, NIPS 2014) 𝐿 = 𝑆𝑜𝑓𝑡𝑚𝑎𝑥 + 𝑉𝑒𝑟𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑉𝑒𝑟𝑖𝑓 = 1 2 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝑆𝑎𝑚𝑒 𝐼𝐷 1 2 max 0, 𝑚 − 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝐷𝑖𝑓𝑓. 𝐼𝐷 Euclidean Space Figure from DeepID2 Paper
  • 17. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepID2 (by HKU, NIPS 2014) 𝐿 = 𝑆𝑜𝑓𝑡𝑚𝑎𝑥 + 𝑉𝑒𝑟𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑉𝑒𝑟𝑖𝑓 = 1 2 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝑆𝑎𝑚𝑒 𝐼𝐷 1 2 max 0, 𝑚 − 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝐷𝑖𝑓𝑓. 𝐼𝐷 Compresses Intra-ID Variance Figure from DeepID2 Paper
  • 18. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepID2 (by HKU, NIPS 2014) 𝐿 = 𝑆𝑜𝑓𝑡𝑚𝑎𝑥 + 𝑉𝑒𝑟𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑉𝑒𝑟𝑖𝑓 = 1 2 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝑆𝑎𝑚𝑒 𝐼𝐷 1 2 max 0, 𝑚 − 𝑓𝑖 − 𝑓𝑗 2 𝑖𝑓 𝐷𝑖𝑓𝑓. 𝐼𝐷 Enlarges Inter-ID Variance Figure from DeepID2 Paper
  • 19. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet DeepID2 (by HKU, NIPS 2014) • Fine-tune for Verification using Joint Bayesian (98.97  99.15%) Table from DeepID2 Paper
  • 20. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Figure from Deep Face Recognition: A Survey Paper Citations >1500 >500 >100
  • 21. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet (by Google, CVPR 2015)
  • 22. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet (by Google, CVPR 2015) Table from LFW Survey Paper
  • 23. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet (by Google, CVPR 2015) Before FaceNet - Training using Identification Loss (+ Contrastive Loss) - Fine Tune (using Metric Learning / Joint Bayesian) FaceNet - Training using Metric Learning
  • 24. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Metric Learning
  • 25. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet (by Google, CVPR 2015) • Very Big Data (~260M) • Very Deep Network (ZFNet, GoogleNet) • No Face Alignment • Single Model • Verification Loss (Metric) Only: Face L2 Embedding - Verification: Thresholding the Distance between the two embeddings - Identification: k-NN Classification - Clustering: k-means Clustering
  • 26. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Verification = Thresholding
  • 27. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Identifiaction = K-NN Classification
  • 28. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Clustering = K-means
  • 29. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet (by Google, CVPR 2015) • Very Big Data (~260M) • Very Deep Network (ZFNet, GoogleNet) • No Face Alignment • Single Model • Verification Loss (Metric) Only: Face L2 Embedding - Verification: Thresholding the Distance between the two embeddings - Identification: k-NN Classification - Clustering: k-means Clustering Triplet Loss
  • 30. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet (by Google, CVPR 2015) Figure from FaceNet Paper
  • 31. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Triplet Loss 𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑝 2 2 Anchor Positive Figures from LFW Dataset
  • 32. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Triplet Loss 𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝛼 − 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑛 2 2 Anchor Negative Figures from LFW Dataset
  • 33. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Triplet Loss 𝑀𝑖𝑛𝑖𝑚𝑖𝑧𝑒 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑝 2 2 − 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑛 2 2 + 𝛼 + For all Triplets Anchor Negative Positive Figures from LFW Dataset
  • 34. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Triplet Loss Training Issue • Large batch size for Anchor-Positive-Negative Balance • Hard Positive in Mini Batch 𝑎𝑟𝑔𝑚𝑎𝑥 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑝 2 2 • Hard Negative in Mini-Batch 𝑎𝑟𝑔𝑚𝑖𝑛 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑛 2 2 Figure from FaceNet Paper
  • 35. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Triplet Loss Training Issue • Large batch size for Anchor-Positive-Negative Balance • Hard Positive in Mini Batch 𝑎𝑟𝑔𝑚𝑎𝑥 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑝 2 2  All Anchor-Positive • Hard Negative in Mini-Batch 𝑎𝑟𝑔𝑚𝑖𝑛 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑛 2 2  Semi-hard 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑝 2 2 < 𝑓 𝑥𝑖 𝑎 − 𝑓 𝑥𝑖 𝑛 2 2 Figure from FaceNet Paper
  • 36. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet FaceNet: Results Figure from FaceNet Paper
  • 37. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Figure from Deep Face Recognition: A Survey Paper Citations >1500 >500 >100
  • 38. VGGFace (by Oxford, BMVC 2015) Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet
  • 39. VGGFace (by Oxford, BMVC 2015) Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Method Images Networks Acc. DeepFace 4M 3 97.35 DeepID3 200 99.47 FaceNet 200M 1 99.63 VGGFace 2.6M 1 98.95 Method Images Networks Acc. DeepFace 4M 3 91.4 DeepID3 200 93.2 FaceNet 200M 1 95.1 VGGFace 2.6M 1 97.3 • VGGFace Dataset (Public Available) • Softmax Loss + Triplet Loss • Compact, SOTA Performance LFW Test Perforamance YTF Test Perforamance Figure from VGGFace Paper
  • 40. Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Figure from Deep Face Recognition: A Survey Paper Citations >1500 >500 >100
  • 41. Discussion / Practical Issues Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet • In FR, Dataset & Pre-processing is Very Very… Important • Metric Learning vs Classification Loss depends on Applications • Open-set / Closed-set • Identification (Facebook) / Verification (Security, Face ID) • Normal FR Research is Saturated
  • 42. Face Recognition vs Object Recognition Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet • Small Inter-class Variations / Large Intra-class Variations (Pose, Emotion, Age) • Small Discriminant Features / Low Resolution / Hard Occlusions • Different Scenarios
  • 43. Metric Learning in Computer Vision = One-shot Learning Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet • Face Recognition • Image Retrieval • Person Re-Identification • Scene Recognition • Object Tracking
  • 44. FR Research Issues Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet Figure from Deep Face Recognition: A Survey Paper
  • 45. References https://github.com/davidsandberg/facenet Tf implementation of FaceNet Yonsei - Image/Video Pattern Recognition LabPR-127: FaceNet
  • 46. Thank You! Image/Video Pattern Recognition Lab School of Electrical & Electronic Engineering