GAIT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORKS

Abstract. In this work we investigate the problem of people recognition by their gait. For this task, we implement deep learning approach using the optical flow as the main source of motion information and combine neural feature extraction with the additional embedding of descriptors for representation improvement. In order to find the best heuristics, we compare several deep neural network architectures, learning and classification strategies. The experiments were made on two popular datasets for gait recognition, so we investigate their advantages and disadvantages and the transferability of considered methods.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Björn W. Schuller,et al.  The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits , 2014, J. Vis. Commun. Image Represent..

[3]  Limin Wang,et al.  Action recognition with trajectory-pooled deep-convolutional descriptors , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[5]  Andrew Zisserman,et al.  Convolutional Two-Stream Network Fusion for Video Action Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Carlos D. Castillo,et al.  Triplet probabilistic embedding for face verification and clustering , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[7]  Tieniu Tan,et al.  Robust view transformation model for gait recognition , 2011, 2011 18th IEEE International Conference on Image Processing.

[8]  Fei Zhang,et al.  Relative distance features for gait recognition with Kinect , 2016, Journal of Visual Communication and Image Representation.

[9]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

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

[11]  Jiansheng Liu,et al.  Average Gait Differential Image Based Human Recognition , 2014, TheScientificWorldJournal.

[12]  Gunnar Farnebäck,et al.  Two-Frame Motion Estimation Based on Polynomial Expansion , 2003, SCIA.

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

[14]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Tianqi Chen,et al.  Net2Net: Accelerating Learning via Knowledge Transfer , 2015, ICLR.

[16]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[17]  Matthew J. Hausknecht,et al.  Beyond short snippets: Deep networks for video classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Chen Wang,et al.  Multiple HOG templates for gait recognition , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).