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.
Many gait recognition methods have been proposed in the last two decades; some are model-based approaches @cite_6 @cite_21 @cite_10 @cite_7 , while others are appearance-based @cite_0 @cite_2 @cite_33 . Model-based approaches mainly focus on modeling a person's walking represented by the sticks and the joints. In appearance-based methods, appearance features are extracted from one gait cycle. The gait energy image @cite_3 is the most common gait feature in the latter group. The GEI is generated by simply averaging the gait silhouettes over one gait cycle. Other appearance-based feature representations include chrono-gait image (CGI) @cite_37 , gait flow image (GFI) @cite_31 , and gait entropy image (GEnI) @cite_12 . The CGI is a temporal template in which the temporal information among gait frames is encoded by a color mapping function. The CGI is obtained by composing the color encoded gait contour images in a gait cycle. The GFI is generated using optical flow to aggregate gray scale contours over one gait cycle. The GEnI represents the randomness of pixel values in the silhouettes image sequence.
@cite_37 @cite_33 @cite_7 @cite_21 @cite_6 @cite_3 @cite_0 @cite_2 @cite_31 @cite_10 @cite_12
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Gait Energy Image (GEI) is an efficient template for human identification by gait. However, such a template loses temporal information in a gait sequence, which is critical to the performance of gait recognition. To address this issue, we develop a novel temporal template, named Chrono-Gait Image (CGI), in this paper. The proposed CGI template first extracts the contour in each gait frame, followed by encoding each of the gait contour images in the same gait sequence with a multichannel mapping function and compositing them to a single CGI. To make the templates robust to a complex surrounding environment, we also propose CGI-based real and synthetic temporal information preserving templates by using different gait periods and contour distortion techniques. Extensive experiments on three benchmark gait databases indicate that, compared with the recently published gait recognition approaches, our CGI-based temporal information preserving approach achieves competitive performance in gait recognition with robustness and efficiency. The paper proposes a two-phase view-invariant multiscale gait recognition method (VI-MGR) which is robust to variation in clothing and presence of a carried item. In phase 1, VI-MGR uses the entropy of the limb region of a gait energy image (GEI) to determine the matching gallery view of the probe using 2-dimensional principal component analysis and Euclidean distance classifier. In phase 2, the probe subject is compared with the matching view of the gallery subjects using multiscale shape analysis. In this phase, VI-MGR applies Gaussian filter to a GEI to generate a multiscale gait image for gradually highlighting the subject?s inner shape characteristics to achieve insensitiveness to boundary shape alterations due to carrying conditions and clothing variation. A weighted random subspace learning based classification is used to exploit the high dimensionality of the feature space for improved identification by avoiding overlearning. Experimental analyses on public datasets demonstrate the efficacy of VI-MGR. HighlightsThe paper proposes a two-phase view-invariant multiscale gait recognition method (VI-MGR).VI-MGR is also robust to clothing variation and presence of a carried item.Phase 1 determines the matching gallery view of the probe using entropy.Phase 2 performs multiscale shape analysis using the Gaussian filter.A subject is classified using weighted random subspace learning to avoid overfitting. In this paper, we propose a new spatio-temporal gait representation, called Gait Energy Image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we also propose a novel approach for human recognition by combining statistical gait features from real and synthetic templates. We directly compute the real templates from training silhouette sequences, while we generate the synthetic templates from training sequences by simulating silhouette distortion. We use a statistical approach for learning effective features from real and synthetic templates. We compare the proposed GEI-based gait recognition approach with other gait recognition approaches on USF HumanID Database. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches In this paper, we propose a novel gait representation-gait flow image (GFI) for use in gait recognition. This representation will further improve recognition rates. The basis of GFI is the binary silhouette sequence. GFI is generated by using an optical flow field without constructing any model. The performance of the proposed representation was evaluated and compared with the other representations, such as gait energy image (GEI), experimentally on the USF data set. The USF data set is a public data set in which the image sequences were captured outdoors. The experimental results show that the proposed representation is efficient for human identification. The average recognition rate of GFI is better than that of the other representations in direct matching and dimensional reduction approaches. In the direct matching approach, GFI achieved an average identification rate 42.83 , which is better than GEI by 3.75 . In the dimensional reduction approach, GFI achieved an average identification rate 43.08 , which is better than GEI by 1.5 . The experimental result showed that GFI is stronger in resisting the difference of the carrying condition compared with other gait representations.