Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (3): 508-519.doi: 10.1007/s11390-021-1325-9

Special Issue: Computer Graphics and Multimedia

• Special Section of CVM 2021 • Previous Articles     Next Articles

Is It Easy to Recognize Baby's Age and Gender?

Yang Liu, Ruili He, Xiaoqian Lv, Wei Wang, Xin Sun, and Shengping Zhang*        

  1. School of Computer Science and Technology, Harbin Institute of Technology, Weihai 264209, China
  • Received:2021-01-27 Revised:2021-04-25 Online:2021-05-05 Published:2021-05-31
  • Contact: Shengping Zhang
  • About author:Yang Liu obtained her Ph.D. degree in computer science from Harbin Institute of Technology, Weihai, in 2015. She is currently a lecturer in the School of Computer Science and Technology at Harbin Institute of Technology, Weihai. Her current research interests include digital image processing and computer vision.
  • Supported by:
    This work was supported in part by the National Natural Science Foundation of China under Nos. 61872112 and 61902092. Shengping Zhang was also supported by the Taishan Scholars Program of Shandong Province under Grant No. tsqn201812106.

Face analysis tasks, e.g., estimating gender or age from a face image, have been attracting increasing interest in recent years. However, most existing studies focus mainly on analyzing an adult's face and ignore an interesting question:is it easy to estimate gender and age from a baby's face? In this paper, we explore this interesting problem. We first collect a new face image dataset for our research, named BabyFace, which contains 15 528 images from 5 872 babies younger than two years old. Besides gender, each face image is annotated with age in months from 0 to 24. In addition, we propose new age estimation and gender recognition methods. In particular, based on SSR-Net backbone, we introduce the attention mechanism module to solve the age estimation problem on the BabyFace dataset, named SSR-SE. In the part of gender recognition, inspired by the age estimation method, we also use a two-stream structure, named Two-Steam SE-block with Augment (TSSEAug). We extensively evaluate the performance of the proposed methods against the state-of-the-art methods on BabyFace. Our age estimation model achieves very appealing performance with an estimation error of less than two months. The proposed gender recognition method obtains the best accuracy among all compared methods. To the best of our knowledge, we are the first to study age estimation and gender recognition from a baby's face image, which is complementary to existing adult gender and age estimation methods and can shed some light on exploring baby face analysis.

Key words: age estimation; gender recognition; baby face; convolutional neural network (CNN); attention mechanism;

[1] Levi G, Hassner T. Age and gender classification using convolutional neural networks. In Proc. the 2015 IEEE International Conference on Computer Vision and Pattern Recognition Workshops, June 2015, pp.34-42. DOI:10.1109/CVPRW.2015.7301352.
[2] Das A, Dantcheva A, Bremond F. Mitigating bias in gender, age and ethnicity classification:A multi-task convolution neural network approach. In Proc. the 2018 European Conference on Computer Vision, Sept. 2018, pp.573-583. DOI:10.1007/978-3-030-11009-3_35.
[3] Hosseini S, Lee S H, Kwon H J, Koo H I, Cho N I. Age and gender classification using wide convolutional neural network and Gabor filter. In Proc. the 2018 International Workshop on Advanced Image Technology, Jan. 2018. DOI:10.1109/IWAIT.2018.8369721.
[4] Agbo-Ajala O, Viriri S. Age group and gender classification of unconstrained faces. In Proc. the 14th International Symposium on Visual Computing, Oct. 2019, pp.418-429. DOI:10.1007/978-3-030-33720-9_32.
[5] Eidinger E, Enbar R, Hassner T. Age and gender estimation of unfiltered faces. IEEE Transactions on Information Forensics and Security, 2014, 9(12):2170-2179. DOI:10.1109/TIFS.2014.2359646.
[6] Huang G B, Mattar M, Berg T, Learned-Miller E. Labeled faces in the wild:A database for studying face recognition in unconstrained environments. In Proc. Workshop on Faces in ‘Real-Life’ Images:Detection, Alignment, and Recognition, Oct. 2008.
[7] Panis G, Lanitis A, Tsapatsoulis N, Cootes T F. Overview of research on facial ageing using the FG-NET ageing database. IET Biometrics, 2016, 5(2):37-46. DOI:10.1049/iet-bmt.2014.0053.
[8] Ricanek K, Tesafaye T. MORPH:A longitudinal image database of normal adult age-progression. In Proc. the 7th International Conference on Automatic Face and Gesture Recognition, Apr. 2006, pp.341-345. DOI:10.1109/FGR.2006.78.
[9] Rothe R, Timofte R, Gool L. DEX:Deep expectation of apparent age from a single image. In Proc. the 2015 IEEE International Conference on Computer Vision Workshops, Dec. 2015, pp.10-15. DOI:10.1109/ICCVW.2015.41.
[10] Cao Q, Li S, Xie W, Parkhi O M, Zisserman A. VGGFace2:A dataset for recognising faces across pose and age. In Proc. the 13th IEEE International Conference on Automatic Face & Gesture Recognition, May 2018. DOI:10.1109/FG.2018.00020.
[11] Phillips D, McCartney K, Scarr S. Child-care quality and children's social development. Developmental Psychology, 1987, 23(4):537-543. DOI:10.1037/0012-1649.23.4.537.
[12] Letourneau N. Scientific Parenting:What Science Reveals About Parental Influence. Dundurn, 2013.
[13] Simpson A R. The role of the mass media in parenting education. Technical Report, Center for Health Communication, Harvard School of Public Health, 1997., Jan. 2021.
[14] Yang T Y, Huang Y H, Lin Y Y, Hsiu P C, Chuang Y Y. Ssr-net:A compact soft stagewise regression network for age estimation. In Proc. the 27th International Joint Conference on Artificial Intelligence, July 2018, pp.1078-1084. DOI:10.24963/ijcai.2018/150.
[15] Zhong Z, Zheng L, Kang G, Li S, Yang Y. Random erasing data augmentation. In Proc. the 34th AAAI Conference on Artificial Intelligence, Feb. 2020, pp.13001-13008. DOI:10.1609/aaai.v34i07.7000.
[16] Zhang H, Cissé M, Dauphin Y N, Lopez-Paz D. mixup:Beyond empirical risk minimization. In Proc. the 6th International Conference on Learning Representations, April 30-May 3, 2018.
[17] Deng J, Dong W, Socher R, Li L J, Li K, Li F F. ImageNet:A large-scale hierarchical image database. In Proc. the 2009 IEEE Conference on Computer Vision I & Pattern Recognition, Jun. 2009. DOI:10.1109/CVPR.2009.5206848.
[18] Chen B C, Chen C S, Hsu W H. Cross-age reference coding for age-invariant face recognition and retrieval. In Proc. the 13th European Conference on Computer Vision, Sept. 2014, pp.768-783. DOI:10.1007/978-3-319-10599-4_49.
[19] Yi D, Lei Z, Li S Z. Age estimation by multi-scale convolutional network. In Proc. the 12th Asian Conference on Computer Vision, Nov. 2014, pp.144-158. DOI:10.1007/978-3-319-16811-110.
[20] Rothe R, Timofte R, Van Gool L. Deep expectation of real and apparent age from a single image without facial landmarks. International Journal of Computer Vision, 2018, 126(2/3/4):144-157. DOI:10.1007/s11263-016-0940-3.
[21] Wang X, Guo R, Kambhamettu C. Deeply-learned feature for age estimation. In Proc. the 2015 IEEE Winter Conference on Applications of Computer Vision, Jan. 2015, pp.534-541. DOI:10.1109/WACV.2015.77.
[22] Zhu H, Zhang Y, Li G, Zhang J, Shan H. Ordinal distribution regression for gait-based age estimation. Science China Information Sciences, 2020, 63(2):Article No. 120102. DOI:10.1007/s11432-019-2733-4.
[23] Niu Z, Zhou M, Wang L, Gao X, Hua G. Ordinal regression with multiple output CNN for age estimation. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2016, pp.4920-4928. DOI:10.1109/CVPR.2016.532.
[24] Lapuschkin S, Binder A, Muller K R, Samek W. Understanding and comparing deep neural networks for age and gender classification. In Proc. the 2017 IEEE International Conference on Computer Vision Workshops, Oct. 2017, pp.1629-1638. DOI:10.1109/ICCVW.2017.191.
[25] Verma A, Vig L. Using convolutional neural networks to discover cogntively validated features for gender classification. In Proc. the 2014 International Conference on Soft Computing and Machine Intelligence, Sept. 2014, pp.33-37. DOI:10.1109/ISCMI.2014.17.
[26] Mansanet J, Albiol A, Paredes R. Local deep neural networks for gender recognition. Pattern Recognition Letters, 2016, 70:80-86. DOI:10.1016/j.patrec.2015.11.015.
[27] Abbas H, Hicks Y, Marshall D, Zhurov A I, Richmond S. A 3D morphometric perspective for facial gender analysis and classification using geodesic path curvature features. Computational Visual Media, 2018, 4(1):17-32. DOI:10.1007/s41095-017-0097-1.
[28] King D E. Dlib-ml:A machine learning toolkit. Journal of Machine Learning Research, 2009, 10:1755-1758.
[29] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment:From error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4):600-612. DOI:10.1109/TIP.2003.819861.
[30] Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In Proc. the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 2018, pp.7132-7141. DOI:10.1109/CVPR.2018.00745.
[31] Kingma D P, Ba J. Adam:A method for stochastic optimization. arXiv:1412.6980, 2014., Jan. 2021.
[32] Ranjan R, Patel V, Chellappa R. 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, 2019, 41(1):121-135. DOI:10.1109/TPAMI.2017.2781233.
[33] Dehghan A, Ortiz E G, Shu G, Masood S Z. Dager:Deep age, gender and emotion recognition using convolutional neural network. arXiv:1702.04280, 2017., Jan. 2021.
[34] Liu Z, Luo P, Wang X, Tang X. Deep learning face attributes in the wild. In Proc. the 2015 IEEE International Conference on Computer Vision, Dec. 2015, pp.3730-3738. DOI:10.1109/ICCV.2015.425.
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[1] Zhou Di;. A Recovery Technique for Distributed Communicating Process Systems[J]. , 1986, 1(2): 34 -43 .
[2] Chen Shihua;. On the Structure of Finite Automata of Which M Is an(Weak)Inverse with Delay τ[J]. , 1986, 1(2): 54 -59 .
[3] C.Y.Chung; H.R.Hwa;. A Chinese Information Processing System[J]. , 1986, 1(2): 15 -24 .
[4] Jin Lan; Yang Yuanyuan;. A Modified Version of Chordal Ring[J]. , 1986, 1(3): 15 -32 .
[5] Wang Jianchao; Wei Daozheng;. An Effective Test Generation Algorithm for Combinational Circuits[J]. , 1986, 1(4): 1 -16 .
[6] Chen Zhaoxiong; Gao Qingshi;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[7] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[8] Zheng Guoliang; Li Hui;. The Design and Implementation of the Syntax-Directed Editor Generator(SEG)[J]. , 1986, 1(4): 39 -48 .
[9] Huang Xuedong; Cai Lianhong; Fang Ditang; Chi Bianjin; Zhou Li; Jiang Li;. A Computer System for Chinese Character Speech Input[J]. , 1986, 1(4): 75 -83 .
[10] Xu Xiaoshu;. Simplification of Multivalued Sequential SULM Network by Using Cascade Decomposition[J]. , 1986, 1(4): 84 -95 .

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