Abstract
Despite promising progress has been achieved on face recognition problems, cross-age face recognition remains a challenging task due to its age variations. Human appearances change along with the age growing process, which increases the difficulty of recognition tasks. Existing methods mainly focus on synthesizing new facial images according to different age levels or isolating age-related features and identity related features. In this paper, we propose an identity-level angular triplet loss for cross-age face recognition. The facial images are projected to an embedding space where the angle between feature embeddings can represent similarities of images. Different from Euclidean distance metric, the angular metric used in our method guides the model to learn discriminative features under large intra-class discrepancy. Angles between intra-class embeddings are reduced while that between inter-class are enlarged. The selection of good triplets is conducted on an identity-level rather than instance-level with a moderate positive mining strategy. Experiments are conducted on cross-age databases and results prove the effectiveness of our method.
Similar content being viewed by others
References
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces VS. Fishedaces 1Kecognitinn using class specific linear projection D. IEEE Trans Pattern Anal Mach Intell 19(7):711
Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In: Proceedings 1991 IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, pp 586–587
Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2008) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210
Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition
Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823
Taigman Y, Yang M, Ranzato M, Wolf L (2014) Deepface: Closing the gap to human-level performance in face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1701–1708
Liu W, Wen Y, Yu Z, Li M, Raj B, Song L (2017) Deep hypersphere embedding for face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 212–220
Deng J, Guo J, Xue N, Zafeiriou S (2019) Arcface: Additive angular margin loss for deep face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4690–4699
Li Z, Park U, Jain AK (2011) A discriminative model for age invariant face recognition. IEEE Trans Inform Forens Secur 6(3):1028
Gong D, Li Z, Tao D, Liu J, Li X (2015) A maximum entropy feature descriptor for age invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5289–5297
Gong D, Li Z, Lin D, Liu J, Tang X (2013) Hidden factor analysis for age invariant face recognition. In: Proceedings of the IEEE international conference on computer vision, pp 2872–2879
Chen B, Chen CS, Hsu WH (2014) Cross-age reference coding for age-invariant face recognition and retrieval. In: European conference on computer vision. Springer, pp 768–783
Wen Y, Li Z, Qiao Y (2016) A discriminative feature learning approach for deep face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4893–4901
Geng X, Zhou Z, Smith-Miles K (2007) Automatic age estimation based on facial aging patterns. IEEE Trans Pattern Anal Mach Intell 29(12):2234
Lanitis A, Taylor CJ, Cootes TF (2002) Automatic face identification system using flexible appearance models. IEEE Trans Pattern Anal Mach Intell 24(4):442
Park U, Tong Y, Jain AK (2010) Age-invariant face recognition. IEEE Trans Pattern Anal Mach Intell 32(5):947
Chao WL, Liu J, Ding JJ (2013) Facial age estimation based on label-sensitive learning and age-oriented regression. Pattern Recogn 46(3):628
Chen D, Cao X, Wen F, Sun J (2013) Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3025–3032
Li Y, Wang G, Nie L, Wang Q, Tan W (2018) Distance metric optimization driven convolutional neural network for age invariant face recognition. Pattern Recogn 75:51
Xu C, Liu Q, Ye M (2017) Age invariant face recognition and retrieval by coupled auto-encoder networks. Neurocomputing 222:62
Li H, Hu H, Yip C (2018) Age-related factor guided joint task modeling convolutional neural network for cross-age face recognition. IEEE Trans Inform Forens Secur 13(9):2383
Wang Y, Gong D, Zhou Z, Ji X, Wang H, Li Z, Liu W, Zhang T (2018) Orthogonal deep features decomposition for age-invariant face recognition. In: Proceedings of the European conference on computer vision (ECCV), pp 738–753
Sun X, Wu P, Hoi SC (2018) Face detection using deep learning: An improved faster RCNN approach. Neurocomputing 299:42
Sun Y, Liang D, Wang X, Tang X (2015) Face recognition with very deep neural networks. arXiv:1502.00873
Sun Y, Wang X, Tang X (2015) Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2892–2900
Fu Y, Hospedales TM, Xiang T, Yao Y, Gong S (2014) Interestingness prediction by robust learning to rank. In: ECCV
Shi H, Yang Y, Zhu X, Liao S, Lei Z, Zheng W, Li SZ (2016) Embedding deep metric for person re-identification: A study against large variations. In: European conference on computer vision. Springer, pp 732–748
Fu Y, Huang TS (2008) Age synthesis and estimation via faces: A survey. IEEE Trans Multimed 10(4):578
Suo J, Chen X, Shan S, Gao W (2009) Learning long term face aging patterns from partially dense aging databases. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 622–629
Du J, Zhai CM, Ye YQ (2013) Face aging simulation and recognition based on NMF algorithm with sparseness constraints. Neurocomputing 116:250
Ling H, Soatto S, Ramanathan N, Jacobs DW (2009) Face verification across age progression using discriminative methods. IEEE Trans Inform Forens Secur 5(1):82
Wang H, Gong D, Li Z, Liu W (2019) Decorrelated adversarial learning for age-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3527–3536
Shakeel MS, Lam KM (2019) Deep-feature encoding-based discriminative model for age-invariant face recognition. Pattern Recogn 93:442
Moustafa AA, Elnakib A, Areed NF (2020) Face recognition using various feature extraction approaches. In: SIViP, pp 1–8
Du L, Hu H (2020) Cross-age identity difference analysis model based on image pairs for age invariant face verification. IEEE Trans Circ Syst Video Technol
Zhao J, Yan S, Feng J (2020) Recognizing profile faces by imagining frontal view. International Journal of Computer Vision
Wen Y, Zhang K, Li Z, Qiao Y (2016) A discriminative feature learning approach for deep face recognition. In: European conference on computer vision. Springer, pp 499–515
Zheng T, Deng W, Hu J (2017) Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments. arXiv:1708.08197
Dai J, He K, Sun J (2016) Instance-aware semantic segmentation via multi-task network cascades. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3150–3158
Chen B, Deng W, Du J (2017) Noisy softmax: Improving the generalization ability of dcnn via postponing the early softmax saturation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5372–5381
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chen, X., Lau, H.Y.K. The identity-level angular triplet loss for cross-age face recognition. Appl Intell 52, 6330–6339 (2022). https://doi.org/10.1007/s10489-021-02742-3
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-021-02742-3