Practical age estimation using deep label distribution learning
Huiying ZHANG1,2, Yu ZHANG2, Xin GENG2()
1. Pujiang Institute, Nanjing Tech University, Nanjing 211200, China 2. School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Age estimation plays an important role in humancomputer interaction system. The lack of large number of facial images with definite age label makes age estimation algorithms inefficient. Deep label distribution learning (DLDL) which employs convolutional neural networks (CNN) and label distribution learning to learn ambiguity from ground-truth age and adjacent ages, has been proven to outperform current state-of-the-art framework. However, DLDL assumes a rough label distribution which covers all ages for any given age label. In this paper, a more practical label distribution paradigm is proposed: we limit age label distribution that only covers a reasonable number of neighboring ages. In addition, we explore different label distributions to improve the performance of the proposed learning model. We employ CNN and the improved label distribution learning to estimate age. Experimental results show that compared to the DLDL, our method is more effective for facial age recognition.
X Geng, C Yin, Z H Zhou. Facial age estimation by learning from label distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2401–2412 https://doi.org/10.1109/TPAMI.2013.51
B B Gao, C Xing, C W Xie, J Wu, X Geng. Deep label distribution learning with label ambiguity. IEEE Transactions on Image Processing, 2017, 26(6): 2825–2838 https://doi.org/10.1109/TIP.2017.2689998
4
X Geng, Q Wang, Y Xia. Facial age estimation by adaptive label distribution learning. In: Proceeding of the 22nd International Conference on Pattern Recognition. 2014, 4465–4470 https://doi.org/10.1109/ICPR.2014.764
Y F Li, D M Liang. Safe semi-supervised learning: a brief introduction. Frontiers of Computer Science, 2019, 13(4): 669–676 https://doi.org/10.1007/s11704-019-8452-2
7
X Y Liu, S T Wang, M L Zhang. Transfer synthetic over-sampling for class-imbalance learning with limited minority class data. Frontiers of Computer Science, 2019, 13(5): 996–1009 https://doi.org/10.1007/s11704-018-7182-1
8
R Zhao, X Niu, Y Wu, W Luk, Q Liu. Optimizing CNN-based object detection algorithms on embedded FPGA platforms. In: Proceedings of the 13th International Symposium on Applied Reconfigurable Computing. 2017, 255–267 https://doi.org/10.1007/978-3-319-56258-2_22
9
Z He, M Kan, J Zhang, X Chen, S Shan. A fully end-to-end cascaded CNN for facial landmark detection. In: Proceeding of the 12th IEEE International Conference on Automatic Face and Gesture Recognition. 2017, 200–207 https://doi.org/10.1109/FG.2017.33
10
D Marmanis, J D Wegner, S Galliani, K Schindler, M Datu, U Stilla. Semantic segmentation of aerial images with an ensemble of CNNs. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, 3(3): 273–480 https://doi.org/10.5194/isprsannals-III-3-473-2016
11
R Ranjan, VM Patel, R Chellappa. HyperFace: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 41(1): 121–135 https://doi.org/10.1109/TPAMI.2017.2781233
12
A Graves, N Jaitly, A R Mohamed. Hybrid speech recognition with deep bidirectional LSTM. IEEE Automatic Speech Recognition and Understanding, 2014, 1(3): 273–278 https://doi.org/10.1109/ASRU.2013.6707742
13
Y Fan, X J Lu, D Li, Y L Liu. Video-based emotion recognition using CNN-RNN and C3D hybrid networks. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction. 2016, 445–450 https://doi.org/10.1145/2993148.2997632
14
Y Chen, X Yang, B Zhong, S Pan, D Chen, H Zhang. CNN tracker: online discriminative object tracking via deep convolutional neural network. Applied Soft Computing, 2016, 2(38): 1088–1098 https://doi.org/10.1016/j.asoc.2015.06.048
15
G Guo, G Mu. Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression. In: Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition. 2011, 657–644 https://doi.org/10.1109/CVPR.2011.5995404
16
G Guo, G Mu. Joint estimation of age, gender and ethnicity: CCA vs. PLS. In: Proceedings of the 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition. 2013, 1–6 https://doi.org/10.1109/FG.2013.6553737
17
W B Horng. Classification of age groups based on facial features. Tamkang Journal of Science and Engineering, 2001, 4(3): 183–192
18
Z A Othman, D A Adnan. Age classification from facial images system. International Journal of Computer Science and Mobile Computing, 2014, 3(10): 291–303
19
Y H Kwon, N D Vitoria Lobo. Age classification from facial images. Computer Vision and Image Understanding, 1999, 74(1): 1–21 https://doi.org/10.1006/cviu.1997.0549
20
T F Cootes, G J Edwards, C J Taylor. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 681–685 https://doi.org/10.1109/34.927467
21
Y Fu, T S Huang. Human age estimation with regression on discriminative aging manifold. IEEE Transactions on Multimedia, 2008, 10(4): 578–584 https://doi.org/10.1109/TMM.2008.921847
22
X Geng, Z H Zhou, K Smithmiles. Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(12): 2234–2240 https://doi.org/10.1109/TPAMI.2007.70733
23
D R Hardoon, S Szedmak, J Shawe-Taylor. Canonical correlation analysis: an overview with application to learning methods. Neural Computation, 2004, 16(12): 2639–2664 https://doi.org/10.1162/0899766042321814
D Basak, P Srimanta, D C Patranabis. Support vector regression. Neural Information Processing-letter and Reviews, 2007, 11(10): 203–224
26
D Yi, Z Lei, S Z Li. Age estimation by multi-scale convolutional network. In: Proceedings of the 12th Asian Conference on Computer Vision. 2014, 144–158 https://doi.org/10.1007/978-3-319-16811-1_10
27
S Chen, C Zhang, M Dong. Deep age estimation: from classification to ranking. IEEE Transactions on Multimedia, 2017, 20(8): 2209–2222 https://doi.org/10.1109/TMM.2017.2786869
28
Z Z Hu, Y G Wen. Facial age estimation with age difference. IEEE Transactions on Image Processing, 2017, 26(7): 3087–3097 https://doi.org/10.1109/TIP.2016.2633868
29
M Duan, K Li, K Lia. An ensemble CNN2ELM for age estimation. IEEE Transactions on Information Forensics and Security, 2017, 99(1): 1–12
30
X Geng, C Yin, Z H Zhou. Facial age estimation by learning from label distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2401–2412 https://doi.org/10.1109/TPAMI.2013.51
31
J K Ricanek, T Tesafaye. Morph: a longitudinal image database of normal adult age-progression. In: Proceedings the 7th International Conference on Automatic Face and Gesture Recognition. 2006, 341–345
32
M Mathias, R Benenson, M Pedersoli, L Van Gool. Face detection without bells and whistles. In: Proceedings of European Conference on Computer Vision. 2014, 720–735 https://doi.org/10.1007/978-3-319-10593-2_47
