Abstract Gait as a biometric property for person identification plays a key role in video surveillance and security applications. In gait recognition, normally, gait feature such as Gait Energy Image (GEI) is extracted from one full gait cycle. However in many circumstances, such a full gait cycle might not be available due to occlusion. Thus, the GEI is not complete giving rise to a degrading in gait-based person identification rate. In this paper, we address this issue by proposing a novel method to identify individuals from gait feature when a few (or even single) frame(s) is available. To do so, we propose a deep learning-based approach to transform incomplete GEI to the corresponding complete GEI obtained from a full gait cycle. More precisely, this transformation is done gradually by training several auto encoders independently and then combining these as a uniform model. Experimental results on two public gait datasets, namely OULP and Casia-B demonstrate the validity of the proposed method in dealing with very incomplete gait cycles.
A behavior classification method based on Enhanced Gait Energy Image (EGEI) and 2-Directional 2-dimensional principal component analysis ((2D)2PCA) was proposed. EGEI extracted more useful feature information. The high dimensional feature space was reduced to lower dimensional space by (2D)2PCA, which outperformed PCA and 2DPCA.The nearest-neighbor classifier was adopted to distinguish different actions. Experimental results showed that the algorithm was simple, and achieved higher classification accuracy with less running time. The strength of gait, compared to other biometrics, is that it does not require cooperative subjects. In previous work gait recognition approaches were evaluated using a gallery set consisting of gait sequences of people under similar covariate conditions (e.g. clothing, surface, carrying, and view conditions). This evaluation procedure, however, implies that the gait data are collected in a cooperative manner so that the covariate conditions are known a priori. In this work, gait recognition approaches are evaluated without the assumption on cooperative subjects, i.e. both the gallery and the probe sets consist of a mixture of gait sequences under different and unknown covariate conditions. The results indicate that the performance of the existing approaches would drop drastically under this more realistic experimental setup. We argue that selecting the most relevant gait features that are invariant to changes in gait covariate conditions is the key to develop a gait recognition system that works without subject cooperation. To this end, Gait Entropy Image (GEnI) is proposed to perform automatic feature selection on each pair of gallery and probe gait sequences. Moreover, an Adaptive Component and Discriminant Analysis (ACDA) is formulated which seamlessly integrates our feature selection method with subspace analysis for robust recognition, and importantly is computationally much more efficient compared to the conventional Component and Discriminant Analysis. Experiments are carried out on two comprehensive benchmarking databases: the CASIA database and the Southampton Human ID at a distance gait database (SOTON database). Our results demonstrate that the proposed approach significantly outperforms the existing techniques particularly when gait is captured with variable and unknown covariate conditions. This paper describes the world's largest gait database-the “OU-ISIR Gait Database, Large Population Dataset”-and its application to a statistically reliable performance evaluation of vision-based gait recognition. Whereas existing gait databases include at most 185 subjects, we construct a larger gait database that includes 4007 subjects (2135 males and 1872 females) with ages ranging from 1 to 94 years. The dataset allows us to determine statistically significant performance differences between currently proposed gait features. In addition, the dependences of gait-recognition performance on gender and age group are investigated and the results provide several novel insights, such as the gradual change in recognition performance with human growth.