Comparative Study of Gait Gender Identification using Gait Energy Image (GEI) and Gait Information Image (GII)

Identifying gender from the pedestrian video is one crucial key to study demographics in such areas. With current video surveillance technology, identifying gender from a distance is possible. This research proposed the utilization of computer vision to identify gender based on their walking gait. The data feature used to determine gender based on their walking gait divided into five parts, namely the head, chest, back, waist & buttocks, and legs. Two different methods are used to perform the real-time gender gait recognition process, i.e., Gait Energy Image (GEI) and Gait Information Image (GII), while the Support Vector Machine (SVM) method used as the data classifier. The experimental results show that the process of identifying gender based on walking with GEI method is 55% accuracy and GII method is 60% accuracy. From these results, it can conclude that the method GII with SVM classifier has the best accuracy in the process of gender classification

[1]  Wang Kejun,et al.  A behavior classification based on Enhanced Gait Energy Image , 2010, 2010 International Conference on Networking and Digital Society.

[2]  Smriti Srivastava,et al.  Gait based authentication using gait information image features , 2015, Pattern Recognit. Lett..

[3]  V. N. Kamalesh,et al.  Human gait recognition using four directional variations of gradient gait energy image , 2016, 2016 International Conference on Computing, Communication and Automation (ICCCA).

[4]  Tieniu Tan,et al.  A Study on Gait-Based Gender Classification , 2009, IEEE Transactions on Image Processing.

[5]  Madasu Hanmandlu,et al.  Content-based Image Retrieval by Information Theoretic Measure , 2011 .

[6]  Kazuyo Tanaka,et al.  Book Recommendation Signage System Using Silhouette-Based Gait Classification , 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

[7]  Ja-Ling Wu,et al.  Real-time Gender Classification from Human Gait for Arbitrary View Angles , 2009, 2009 11th IEEE International Symposium on Multimedia.

[8]  Li-Hong Juang,et al.  Gender Recognition Studying by Gait Energy Image Classification , 2012, 2012 International Symposium on Computer, Consumer and Control.

[9]  Yahui Wang,et al.  Using multiple views for gait-based gender classification , 2014, The 26th Chinese Control and Decision Conference (2014 CCDC).