Silhouette-Based Gender Recognition in Smart Environments Using Fuzzy Local Binary Patterns and Support Vector Machines

Automatic recognition of human gender in smart environments is an interesting problem in biometric and demographic studies. In this paper, we describe a method for gender recognition at a distance based on visual texture analysis of gait energy images (GEIs). These images summarize the structural and dynamical variations of the subject's silhouette during one gait cycle. Texture analysis and feature extraction are based on histogram calculation of fuzzy local binary pattern (FLBP), which describes the relative intensities of each pixel with surrounding neighbors. Unlike the original LBP, each pixel can contribute, with different weights, to more than one bin in the histogram of occurring codes. The classification model uses support vector machines with linear kernel function. The performance of the proposed approach is intensively evaluated and compared with other texture on CASIA B multiview gait database. We also consider the variation of some conditions such as clothing and carried objects. Results show that the proposed approach is promising and outperforms other variants in representing texture for gait-based gender recognition.