Gait-Based Age Estimation with Deep Convolutional Neural Network

Gait is a unique biometric identifier for its non-invasive and low-cooperative features. Gait-based attribute recognition can play a crucial role in a wide range of applications, such as intelligent surveillance and criminal retrieval. However, due to the lack of data, there are relatively few studies which apply deep convolutional neural networks on gait attribute recognition. In this study, with the new progress in public gait dataset, we proposed a deep convolutional neural network with multi-task learning for gait-based human age estimation. Gait energy images are directly fed into our model for age estimation while gender information is also integrated for improving the performance of age estimation. The experiments on large-scale OULP-Age dataset show that our model outperforms the state-of-the-art.

[1]  Rafael Medina Carnicer,et al.  Deep multi-task learning for gait-based biometrics , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[2]  Yasushi Makihara,et al.  Gait-based age estimation using a whole-generation gait database , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[3]  Xiang Li,et al.  Gait-based human age estimation using age group-dependent manifold learning and regression , 2018, Multimedia Tools and Applications.

[4]  Yousra Ben Jemaa,et al.  Gait-based human age classification using a silhouette model , 2018, IET Biom..

[5]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[6]  Yasushi Makihara,et al.  Gait Analysis of Gender and Age Using a Large-Scale Multi-view Gait Database , 2010, ACCV.

[7]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[8]  Zheng Liu,et al.  Feature map pooling for cross-view gait recognition based on silhouette sequence images , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).

[9]  Jiwen Lu,et al.  Gait-Based Human Age Estimation , 2010, IEEE Transactions on Information Forensics and Security.

[10]  Thian Song Ong,et al.  A preliminary study of gait-based age estimation techniques , 2015, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA).

[11]  Xiang Li,et al.  Joint Intensity and Spatial Metric Learning for Robust Gait Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Yousra Ben Jemaa,et al.  Gait features fusion for efficient automatic age classification , 2018, IET Comput. Vis..

[13]  Bir Bhanu,et al.  Individual recognition using gait energy image , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Xiaogang Wang,et al.  A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Daksh Thapar,et al.  VGR-net: A view invariant gait recognition network , 2017, 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA).

[17]  Manuel J. Marín-Jiménez,et al.  Automatic Learning of Gait Signatures for People Identification , 2016, IWANN.

[18]  Yasushi Makihara,et al.  GEINet: View-invariant gait recognition using a convolutional neural network , 2016, 2016 International Conference on Biometrics (ICB).

[19]  Gunawan Ariyanto,et al.  Model-based 3D gait biometrics , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[20]  Yasushi Makihara,et al.  Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition , 2018, IPSJ Transactions on Computer Vision and Applications.

[21]  Yann LeCun,et al.  Convolutional networks and applications in vision , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[22]  Rajesh Kumar,et al.  A hybrid approach for gait based gender classification using gei and spatio temporal parameters , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[23]  Frederick R. Forst,et al.  On robust estimation of the location parameter , 1980 .

[24]  Wu Liu,et al.  Siamese neural network based gait recognition for human identification , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[25]  Xiaoou Tang,et al.  Facial Landmark Detection by Deep Multi-task Learning , 2014, ECCV.

[26]  Xiang Li,et al.  The OU-ISIR Gait Database comprising the Large Population Dataset with Age and performance evaluation of age estimation , 2017, IPSJ Transactions on Computer Vision and Applications.