Gait-based age progression/regression: a baseline and performance evaluation by age group classification and cross-age gait identification

Gait is believed to be an advanced behavioral biometric that can be perceived at a large distance from a camera without subject cooperation and hence is favorable for many applications in surveillance and forensics. However, appearance differences caused by human aging may significantly reduce the performance of gait recognition. Modeling the aging process on gait features is one of the possible solutions to this problem, and it may inspire more potential applications, such as finding lost children and examining health status. To the best of our knowledge, this topic has not been studied in the literature. Motivated by the fact that aging effects are mainly reflected in the shape and appearance deformations of the gait feature, we propose a baseline algorithm for gait-based age progression and regression using a generic geometric transformation between different age groups, in conjunction with the gait energy image, which is an appearance-based gait feature frequently used in the gait analysis community, to render gait aging and reverse aging effects simultaneously. Various evaluations were conducted through gait-based age group classification and cross-age gait identification to validate the performance of the proposed method, in addition to providing several insights for future research on the subject.