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.
In addition, there are also some gait feature representations based on the GEI including enhanced gait energy image (EGEI) @cite_36 , and masked gait energy image (MGEI) @cite_30 . The EGEI retains the dynamic region information to enhance the GEI feature. The MGEI extracts the dynamic information of a gait sequence based on computed entropy. The MGEI is computed based on the GEI and the entropy of every pixel in order to create a binary feature selection mask of GEI. Iwama et. al @cite_11 show that the GEI is more effective in gait recognition than other gait feature representations.
@cite_36 @cite_30 @cite_11
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Gait Energy Image (GEI) has been proved to be an effective identity signature in gait recognition. But previous approaches only treat this 2D image representation as a holistic feature and neglect the intrinsic dynamic characteristics of gait patterns. In this paper, we use variation analysis to obtain the dynamic region in GEI which reflects the walking manner of an individual. Based on this analysis, a dynamics weight mask is constructed to enhance the dynamic region and suppress the noises on the unimportant regions. The obtained gait representation called enhanced GEI (EGEI) is then represented in low dimensional subspace by Gabor-based discriminative common vectors analysis. We test the proposed approach on the USF HumanID Gait Database. Experimental results prove its effectiveness in terms of recognition rate. Using gait recognition methods, people can be identified by the way they walk. The most successful and efficient of these methods are based on the Gait Energy Image (GEI). In this paper, we extend the traditional Gait Energy Image by including depth information. First, GEI is extended by calculating the required silhouettes using depth data. We then formulate a completely new feature, which we call the Depth Gradient Histogram Energy Image (DGHEI). We compare the improved depth-GEI and the new DGHEI to the traditional GEI. We do this using a new gait database which was recorded with the Kinect sensor. On this database we show significant performance gain of DGHEI. 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.