Gait-based human age classification using a silhouette model

Age estimation at a distance has potential applications including visual surveillance and monitoring in public places. Far from the camera, image resolution is significantly degraded. In fact, age estimation using classical methods such as face is not reliable. Given that gait is very sensitive to ageing, gait analysis is the suitable solution for age estimation at a great distance from the camera. Medical and biomechanical studies prove that older adults adapt their walking toward a safer and more stable gait and an established balance. Indeed, in this study the authors propose a gait-based descriptor for age classification using a silhouette projection model. The proposed model encapsulates both spatiotemporal longitudinal (SLP) and transverse (STP) projections of the silhouette during a gait cycle. The proposed model aims to represent the arms' swing, the head's pitch, the hunched posture and the stride's length, which are among the most outstanding ageing characteristics that appear on the elderly's gait. Although age classification using gait is a very challenging task, SLP and STP curves analysis shows a considerable discrimination between young and elderly people. Also, experiments conducted on the OU-ISIR database prove that their proposed descriptor outperforms existing ones by reaching an important recognition rate.