Chi Xu 교수

In this paper, we propose a pairwise spatial transformer network (PSTN) for cross-view gait recognition, which reduces unwanted feature mis-alignment due to view differences before a recognition step for better performance. The proposed PSTN is a unified CNN architecture that consists of a pairwise spatial transformer (PST) and subsequent recognition network (RN). More specifically, given a matching pair of gait features from different source and target views, the PST estimates a non-rigid defor

Abstract In this paper, we describe the world’s largest gait database, the “OU-ISIR Gait Database, Large Population Dataset with Age (OULP-Age)” and its application to a statistically reliable performance evaluation of gait-based age estimation. Whereas existing gait databases include only 4016 subjects at most, we constructed an extremely large-scale gait database that includes 63,846 subjects (31,093 males and 32,753 females) with ages ranging from 2 to 90 years old. Benchmark algorithms of ga

Partial occlusion of the human body caused by obstacles or a limited camera field of view often occurs in surveillance videos, which affects the performance of gait recognition in practice. Existing methods for gait recognition against occlusion require a bounding box or the height of a full human body as a prerequisite, which is unobserved in occlusion scenarios. In this paper, we propose an occlusion-aware model-based gait recognition method that works directly on gait videos under occlusion w

In this paper, we propose a unified real-time framework for gait-based age estimation and gender classification that uses just a single image, which reduces the latency in video capturing compared with the existing methods based on a gait cycle. To cope with the problem of lacking motion information in the input single image, we first reconstruct a gait cycle of a silhouette sequence from the input image via a gait cycle reconstruction network. The reconstructed gait cycle is then fed into a sta

Gait is believed to be an advanced behavioral biometric that can be perceived at a large distance from a camera without subject cooperation and hence is favorable for many applications in surveillance and forensics. However, appearance differences caused by human aging may significantly reduce the performance of gait recognition. Modeling the aging process on gait features is one of the possible solutions to this problem, and it may inspire more potential applications, such as finding lost child

Gait-based age estimation is a key technique for many applications. It is well known that age estimation uncertainty is highly dependent on age (i.e., small for children and large for adults), and it is important to know the uncertainty for the above-mentioned applications. Therefore, we propose a method for uncertainty-aware gait-based age estimation by introducing a label distribution learning framework. Specifically, we design a network that takes an appearance-based gait feature as input and

Gait is one of the most popular behavioral biometrics because it can be authenticated at a distance from a camera without subject cooperation. Speed differences between matching pairs, however, cause significant performance drops in gait recognition, and gait mode difference (i.e., walking versus running) makes gait recognition further challenging. We therefore propose a speed-invariant gait representation called single-support GEI (SSGEI), which realizes a good trade-off between speed invarianc

Gait recognition tasks often face significant difficulties caused by partial occlusions of the human body. To address this challenge, we propose a silhouette registration method based on flexible estimation of the spatial scale associated with the occluding elements. Existing appearance-based methods require prior knowledge about the spatial scale of the human body in relation to the input image, or a bounding box that includes the actual full body. In our method, the region corresponding to the

Gait recognition has been a hot topic of extensive research in video-based surveillance and forensics. Compared with traditional rectilinear cameras mainly used in existing studies, fisheye cameras have a wider field of view, and hence are more suitable for gait recognition applications in navigation robots, which enables more flexible and free surveillance scenarios. In this paper, to the best of our knowledge, we propose the first framework for gait recognition from images captured by fisheye

With the database technology, artificial intelligence and mathematical statistics the development of technology, database data mining technology arises at the historic moment. This paper presented related formal definitions of association rules and the basic algorithm for association rules mining in data streams. Based on systematic investigation of association rules mining researches on streams data, analyzed issues and how they were resolved in current literatures. Also discussed the future di

In this paper, we propose a unified convolutional neural network (CNN) framework for robust gait recognition against posture changes (e.g., those induced by walking speed changes). In order to mitigate the posture changes, we first register an input matching pair of gait features with different postures by a deformable registration network, which estimates a deformation field to transform the input pair both into their intermediate posture. The pair of the registered features is then fed into a