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Discriminative metric learning for face verification using enhanced Siamese neural network

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Abstract

Although face verification algorithms have made great success under controlled conditions in recent years, there’s plenty of room at its performance under uncontrolled real-world due to lack of discriminative feature representation ability. From the perspective of metric learning, we proposed a context-aware based Siamese neural network (CASNN) to learn a simple yet powerful network for face verification task to enhance its discriminative feature representation ability. Firstly, a context-aware module is used to automatically focus on the key area of the input facial images without irrelevant background area. Then we design a Siamese network equipped with center-classification loss to compress intra-class features and enlarge between-class ones for discriminative metric learning. Finally, we propose a quantitative indicator named “D-score” to show the discriminative representation ability of the learnt features from different methods. The extensive experiments are conducted on LFW dataset, YouTube Face dataset (YTF) and real-world dataset. The results confirm that CASNN outperforms some state-of-the-art deep learning-based face verification methods.

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References

  1. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: Application to face recognition[J]. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  Google Scholar 

  2. Bledsoe WW (1966) The model method in facial recognition[J]. Panoramic Research Inc., Palo Alto, Rep. PR1 15(47):2

  3. Bromley J, Guyon I, LeCun Y et al (1994) Signature verification using a” siamese” time delay neural network[C]. In: Advances in neural information processing systems, pp 737–744

  4. Brunelli R, Poggio T (1993) Face recognition: Features versus templates[J]. IEEE Trans Pattern Anal Mach Intell 15(10):1042–1052

    Article  Google Scholar 

  5. Chen D, Cao X, Wen F et al (2013) Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3025–3032

  6. Chen S, Liu Y, Gao X et al (2018) Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices[C]. In: Chinese Conference on Biometric Recognition. Springer, Cham, pp 428–438

  7. Chopra S, Hadsell R, LeCun Y (2005) Learning a similarity metric discriminatively, with application to face verification[C]. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). IEEE 1:539–546

  8. Cox IJ, Ghosn J, Yianilos PN (1996) Feature-based face recognition using mixturedistance[C]. In: Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp 209–216

  9. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection[C]. 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05). IEEE 1:886–893

  10. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection[C]. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05). IEEE 1:886–893

  11. Deng J, Guo J, Xue N et al (2019) Arcface: Additive angular margin loss for deep face recognition[C]. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4690–4699

  12. Goldstein AJ, Harmon LD, Lesk AB (1971) Identification of human faces[J]. Proc IEEE 59(5):748–760

  13. Guo Y, Zhang L, Hu Y et al (2016) Ms-celeb-1m: A dataset and benchmark for large-scale face recognition[C]. In: European conference on computer vision. Springer, Cham, pp 87–102

  14. He X, Yan S, Hu Y et al (2005) Face recognition using laplacianfaces[J]. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  15. Hu J, Lu J, Tan YP (2014) Discriminative deep metric learning for face verification in the wild[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1875–1882

  16. Huang GB, Learned-Miller E (2014) Labeled faces in the wild: Updates and new reporting procedures[J]. Dept. Comput. Sci., Univ. Massachusetts Amherst, Amherst, MA, USA, Tech. Rep :14–003

  17. Huang GB, Mattar M, Berg T et al (2008) Labeled faces in the wild: A database forstudying face recognition in unconstrained environments[C]

  18. Kanade T (1974) Picture processing system by computer complex and recognition of human faces[J]

  19. Koch G, Zemel R, Salakhutdinov R (2015) Siamese neural networks for one-shot image recognition[C]. In: ICML deep learning workshop, 2

  20. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks[C]. In: Advances in neural information processing systems, pp 1097–1105

  21. Lee K, Chung Y, Byun H (2002) SVM-based face verification with feature set of small size[J]. Electron Lett 38(15):787–789

    Article  Google Scholar 

  22. Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition[J]. IEEE Trans Image Process 11(4):467–476

    Article  Google Scholar 

  23. Liu W, Wen Y, Yu Z et al (2017) Sphereface: Deep hypersphere embedding for face recognition[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 212–220

  24. Lowe DG (1999) Object recognition from local scale-invariant features[C]. In: Proceedings of the seventh IEEE international conference on computer vision. IEEE 2:1150–1157

  25. Lu J, Plataniotis KN, Venetsanopoulos AN et al (2006) Ensemble-based discriminant learning with boosting for face recognition[J]. IEEE Trans Neural Netw 17(1):166–178

    Article  Google Scholar 

  26. Maaten L, Hinton G (2008) Visualizing data using t-SNE[J]. J Mach Learn Res 9(Nov):2579–2605

    MATH  Google Scholar 

  27. Masi I, Wu Y, Hassner T et al (2018) Deep face recognition: A survey[C]//2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, 471–478

  28. Mnih V, Heess N, Graves A (2014) Recurrent models of visual attention[C]. In: Advances in neural information processing systems, pp 2204–2212

  29. Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines[C]. ICML

  30. Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition[J]

  31. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823

  32. Sun Y, Liang D, Wang X et al (2015) Deepid3: Face recognition with very deep neural networks[J]. arXiv preprint arXiv:1502.00873

  33. Sun Y, Chen Y, Wang X et al (2014) Deep learning face representation by joint identification-verification[C]. In: Advances in neural information processing systems, pp 1988–1996

  34. Taigman Y, Yang M, Ranzato MA et al (2014) Deepface: Closing the gap to human-level performance in face verification[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1701–1708

  35. Turk MA, Pentland AP (1991) Face recognition using eigenfaces[C]. IEEE Conference on Computer Vision and Pattern Recognition, pp 586–591

  36. Wen Y, Zhang K, Li Z et al (2016) A discriminative feature learning approach for deep face recognition[C]. In: European conference on computer vision. Springer, Cham, pp 499–515

  37. Wolf L, Hassner T, Maoz I (2011) Face recognition in unconstrained videos with matched background similarity[C]. CVPR 2011. IEEE :529–534

  38. Wright J, Yang AY, Ganesh A et al (2008) Robust face recognition via sparse representation[J]. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  39. Wu X, He R, Sun Z et al (2018) A light cnn for deep face representation with noisy labels[J]. IEEE Trans Inf Forensics Secur 13(11):2884–2896

    Article  Google Scholar 

  40. Zhang K, Zhang Z, Li Z et al (2016) Joint face detection and alignment using multitask cascaded convolutional networks[J]. IEEE Signal Process Lett 23(10):1499–1503

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (61502354, 61771353), Central Support Local Projects of China (2018ZYYD059), the Natural Science Foundation of HubeiProvince of China (2014CFA130, 2015CFB451), Scientific Research Foundation of Wuhan Institute of Technology (K201713), The 10th Graduate Education Innovation Fund of Wuhan Institute of Technology(CX2018213), Hubei Province Technological Innovation Project(2019AAA045), Wuhan Science and Technology Bureau Project (202001602011971).

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  1. Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, 430073, China

    Tao Lu, Qiang Zhou, Wenhua Fang & Yanduo Zhang

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  1. Tao Lu
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  2. Qiang Zhou
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  3. Wenhua Fang
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  4. Yanduo Zhang
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Correspondence to Tao Lu.

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Lu, T., Zhou, Q., Fang, W. et al. Discriminative metric learning for face verification using enhanced Siamese neural network. Multimed Tools Appl 80, 8563–8580 (2021). https://doi.org/10.1007/s11042-020-09784-8

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