Abstract
Person identification is a challenging task in computer vision. Identify a person from different cameras due to changes in appearance based on cofactors. Cofactors such as changing clothes, a suitcase, backpack, etc. The gait biometric is used to identify a person vary with different cofactors at different backgrounds. The person's gait can be identified at a distance, based on a walking pattern, without any physical contact. In this work, the videos are recorded using Infrared and Visible cameras at different locations such as urban and rural environments. The pre-processing includes the recorded videos are converted into frames, person identification using deep learning techniques, background subtraction, artifacts removal, silhouettes extraction, calculating gait cycle, and synthesis frequency domain gait energy image by averaging the silhouettes. The moving features are extracted from the frequency domain gait energy image and gait energy image are dimensionally reduced by principal component analysis, recognized using different classifiers and results are compared. Experiments are conducted on urban and rural datasets recorded using Long Wave Infrared and Visible cameras.
Speed-Invariant Gait Recognition Using Single-Support Gait Energy Image
