A Large RGB-D Gait Dataset and the Baseline Algorithm

With the development of depth sensors, images with high quality depth can be obtained easily. Using depth information, some challenging problems in gait recognition can be reconsidered and better solutions can be developed. To prompt gait recognition with depth information, a large RGB-D gait dataset is introduced. It contains 99 subjects, with 8 sequences for each subjects in two different views. A baseline algorithm, namely Gait Energy Surface (GES), is proposed for researchers to evaluate their own algorithms. Even it is a baseline algorithm, encouraging experimental results have been achieved.

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

[2]  Xuelong Li,et al.  General Tensor Discriminant Analysis and Gabor Features for Gait Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Sridha Sridharan,et al.  Gait energy volumes and frontal gait recognition using depth images , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[4]  R. Venkatesh Babu,et al.  Human gait recognition using depth camera: a covariance based approach , 2012, ICVGIP '12.

[5]  Laura Igual,et al.  Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition , 2012, ICIAR.

[6]  Shamik Sural,et al.  Pose Depth Volume extraction from RGB-D streams for frontal gait recognition , 2014, J. Vis. Commun. Image Represent..

[7]  Mohamed S. Kamel,et al.  Image Analysis and Recognition , 2014, Lecture Notes in Computer Science.

[8]  Tieniu Tan,et al.  Silhouette Analysis-Based Gait Recognition for Human Identification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Luís Ducla Soares,et al.  Frontal gait recognition combining 2D and 3D data , 2012, MM&Sec '12.

[10]  Tieniu Tan,et al.  Fusion of static and dynamic body biometrics for gait recognition , 2004, IEEE Trans. Circuits Syst. Video Technol..