Gait is one of well recognized biometrics that has been widely used for human identification. However, the current gait recognition might have difficulties due to viewing angle being changed. This is because the viewing angle under which the gait signature database was generated may not be the same as the viewing angle when the probe data are obtained. This paper proposes a new multi-view gait recognition approach which tackles the problems mentioned above. Being different from other approaches of same category, this new method creates a so called View Transformation Model (VTM) based on spatial-domain Gait Energy Image (GEI) by adopting Singular Value Decomposition (SVD) technique. To further improve the performance of the proposed VTM, Linear Discriminant Analysis (LDA) is used to optimize the obtained GEI feature vectors. When implementing SVD there are a few practical problems such as large matrix size and over-fitting. In this paper, reduced SVD is introduced to alleviate the effects caused by these problems. Using the generated VTM, the viewing angles of gallery gait data and probe gait data can be transformed into the same direction. Thus, gait signatures can be measured without difficulties. The extensive experiments show that the proposed algorithm can significantly improve the multiple view gait recognition performance when being compared to the similar methods in literature.

Gait is a well recognized biometric feature that is used to identify a human at a distance. However, in real environment, appearance changes of individuals due to viewing angle changes cause many difficulties for gait recognition. This paper re-formulates this problem as a regression problem. A novel solution is proposed to create a View Transformation Model (VTM) from the different point of view using Support Vector Regression (SVR). To facilitate the process of regression, a new method is proposed to seek local Region of Interest (ROI) under one viewing angle for predicting the corresponding motion information under another viewing angle. Thus, the well constructed VTM is able to transfer gait information under one viewing angle into another viewing angle. This proposal can achieve viewindependent gait recognition. It normalizes gait features under various viewing angles into a common viewing angle before similarity measurement is carried out. The extensive experimental results based on widely adopted benchmark dataset demonstrate that the proposed algorithm can achieve significantly better performance than the existing methods in literature.

One of the most challenging issues in color guided depth map restoration is the inconsistency between color edges in guidance color images and depth discontinuities on depth maps. This makes the restored depth map suffer from texture copy artifacts and blurring depth discontinuities. To handle this problem, most state-of-the-art methods design complex guidance weight based on guidance color images and heuristically make use of the bicubic interpolation of the input depth map. In this paper, we show that using bicubic interpolated depth map can blur depth discontinuities when the upsampling factor is large and the input depth map contains large holes and heavy noise. In contrast, we propose a robust optimization framework for color guided depth map restoration. By adopting a robust penalty function to model the smoothness term of our model, we show that the proposed method is robust against the inconsistency between color edges and depth discontinuities even when we use simple guidance weight. To the best of our knowledge, we are the first to solve this problem with a principled mathematical formulation rather than previous heuristic weighting schemes. The proposed robust method performs well in suppressing texture copy artifacts. Moreover, it can better preserve sharp depth discontinuities than previous heuristic weighting schemes. Through comprehensive experiments on both simulated data and real data, we show promising performance of the proposed method.

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Current person re-identification (re-ID) works mainly focus on the short-term scenario where a person is less likely to change clothes. However, in the long-term re-ID scenario, a person has a great chance to change clothes. A sophisticated re-ID system should take such changes into account. To facilitate the study of long-term re-ID, this paper introduces a large-scale re-ID dataset called "Celeb-reID" to the community. Unlike previous datasets, the same person can change clothes in the proposed Celeb-reID dataset. Images of Celeb-reID are acquired from the Internet using street snap-shots of celebrities. There is a total of 1,052 IDs with 34,186 images making Celeb-reID being the largest long-term re-ID dataset so far. To tackle the challenge of cloth changes, we propose to use vector-neuron (VN) capsules instead of the traditional scalar neurons (SN) to design our network.Compared with SN, one extra-dimensional information in VN can perceive cloth changes of the same person. We introduce a well-designed ReIDCaps network and integrate capsules to deal with the person re-ID task. Soft Embedding Attention (SEA) and Feature Sparse Representation (FSR) mechanisms are adopted in our network for performance boosting.Experiments are conducted on the proposed long-term re-ID dataset and two common short-term re-ID datasets. Comprehensive analyses are given to demonstrate the challenge exposed in our datasets. Experimental results show that our ReIDCaps can outperform existing state-of-the-art methods by a large margin in the long-term scenario. The new dataset and code will be released to facilitate future researches.