Person Recognition Using Soft Biometric

Abstract

Global security concerns raised the multiplication of video surveillance devices. Intelligent systems identify the person captured at the different cameras, angles, views, background, and wearing different accessories. Gait is measured at a distance without human cooperation. In this work, gait recognition is increased by concatenating semantic features with traditional features. Combining features are dimensionally reduced and classified using classifiers. This method motivates in future to increase the gait recognition with less false positive detection.