On the distance metric learning between cross-domain gaits

Gait recognition degrades dramatically when gaits are captured from different directions or at different distances due to the low similarity between the registration and the query. This paper addresses the distance metric learning problem in matching between dual cross-domain gaits. Most existing distance metric learning algorithms are only able to match among a set of single domain gaits, but fail to measure the similarity of cross-domain gaits. Traditional gait recognition faces serious challenges, such as various low resolution images, which is caused by acquisition at different distances or different sampling devices, and various body shapes captured by different direction cameras. This paper presents a novel nonlinear coupled mappings (NCMs) algorithm to successfully match between the cross-domain gaits. The relationships within the training data as nodes in a graph are modeled in the kernel space and the constraint is designed to make the difference minimized between cross-domain gaits for an identical subject. Meanwhile, it makes the cross-domain gaits for different subjects disperse more separately with a large margin by using the supervised similarity matrix. Comprehensive experiments show that the proposed algorithm obtains higher accuracy than state-of-the-art algorithms.