Gait-based gender classification using mixed conditional random field.

This paper proposes a supervised modeling approach for gait-based gender classification. Different from traditional temporal modeling methods, male and female gait traits are competitively learned by the addition of gender labels. Shape appearance and temporal dynamics of both genders are integrated into a sequential model called mixed conditional random field (CRF) (MCRF), which provides an open framework applicable to various spatiotemporal features. In this paper, for the spatial part, pyramids of fitting coefficients are used to generate the gait shape descriptors; for the temporal part, neighborhood-preserving embeddings are clustered to allocate the stance indexes over gait cycles. During these processes, we employ evaluation functions like the partition index and Xie and Beni's index to improve the feature sparseness. By fusion of shape descriptors and stance indexes, the MCRF is constructed in coordination with intra- and intergender temporary Markov properties. Analogous to the maximum likelihood decision used in hidden Markov models (HMMs), several classification strategies on the MCRF are discussed. We use CASIA (Data set B) and IRIP Gait Databases for the experiments. The results show the superior performance of the MCRF over HMMs and separately trained CRFs.