Gait Recognition Based Online Person Identification in a Camera Network
01 Nov 2014-pp 145-156
TL;DR: An online method wherein the gait space of individuals are created as they are tracked, and person identification is carried out on-the-fly based on the uniqueness of gait, using Grassmann discriminant analysis.
Abstract: In this paper, we propose a novel online multi-camera framework for person identification based on gait recognition using Grassmann Discriminant Analysis. We propose an online method wherein the gait space of individuals are created as they are tracked. The gait space is view invariant and the recognition process is carried out in a distributed manner. We assume that only a fixed known set of people are allowed to enter the area under observation. During the training phase, multi-view data of each individual is collected from each camera in the network and their global gait space is created and stored. During the test phase, as an unknown individual is observed by the network of cameras, simultaneously or sequentially, his/her gait space is created. Grassmann manifold theory is applied for classifying the individual. The gait space of an individual is a point on a Grassmann manifold and distance between two gait spaces is the same as distance between two points on a Grassmann manifold. Person identification is, therefore, carried out on-the-fly based on the uniqueness of gait, using Grassmann discriminant analysis.
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01 Jan 1979
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.
1,758 citations
TL;DR: The proposed method, which combines multiview matrix representation and a novel randomized kernel extreme learning machine, is an end-to-end solution for view change problem under Grassmann manifold treatment and outperforms several state-of-the-arts methods.
Abstract: Gait recognition appears to be a valuable asset when conventional biometrics cannot be employed. Nonetheless, recognizing human by gait is not a trivial task due to the complex human kinematic structure and other external factors affecting human locomotion. A major challenge in gait recognition is view variation. A large difference between the views in the query and reference sets often leads to performance deterioration. In this paper, we show how to generate virtual views to compensate the view difference in the query and reference sets, making it possible to match the query and reference sets using standardized views. The proposed method, which combines multiview matrix representation and a novel randomized kernel extreme learning machine, is an end-to-end solution for view change problem under Grassmann manifold treatment. Under the right condition, the view-tagging problem can be eliminated. Since the recording angle and walking direction of the subject are not always available, this is particularly valuable for a practical gait recognition system. We present several working scenarios for multiview recognition that have not be considered before. Rigorous experiments have been conducted on two challenging benchmark databases containing multiview gait datasets. Experiments show that the proposed approach outperforms several state-of-the-arts methods.
42 citations
Cites background from "Gait Recognition Based Online Perso..."
...Recently, Choudhary and Chaudhury [31] have employed the idea in [30] to perform gait recognition in a camera network....
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20 Nov 2016
TL;DR: The Gait Gate is introduced as the first online walk-through access control system based on multimodal biometric person verification based on real-time requirements and mutual subspace method has been used for the face matcher.
Abstract: This paper introduces the Gait Gate as the first online walk-through access control system based on multimodal biometric person verification. Face, gait and height modalities are simultaneously captured by a single RGB-D sensor and fused at the matching-score level. To achieve the real-time requirements, mutual subspace method has been used for the face matcher. An acceptance threshold has been learned beforehand using data of a set of subjects disjoint from the targets. The Gait Gate has been evaluated through experiments in actual online situation. In experiments, 1324 walking sequences have resulted from the verification of 26 targets. The verification results show an average computation time of less than 13 ms and an accuracy of 6.08% FAR and 7.21% FRR.
Cites background from "Gait Recognition Based Online Perso..."
...Although a gait recognition system aiming at working in an online situation has been introduced [21], it was not actually tested online....
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13 Nov 2015
TL;DR: A modified MFA is proposed for gait recognition, where the discriminant classification information is computed to guide the procedure of extracting intrinsic low-dimensional features and provides a linear projection matrix.
Abstract: Gait is a kind of biometric feature to identify a walking person at a distance. As an important biometric feature, human gait has great potential in video-surveillance-based applications, which aims to recognize people by a sequence of walking images. Compared with other biometric feature identifications such as face, fingerprint or iris, in medium to long distance security and surveillance applications in public space, the most important advantage of gait identification is that it can be done at a distance. As gait images are complex, time-varying, high-dimensionality and nonlinear data, many classical pattern recognition methods cannot be applied to gait recognition directly. The main problem in gait recognition asks is dimensionality reduction. Marginal Fisher analysis (MFA) is an efficient and robust dimensionality reduction algorithm. However, MFA does not take the data distribution into consideration. Based on original MFA, a modified MFA is proposed for gait recognition. Firstly, the discriminant classification information is computed to guide the procedure of extracting intrinsic low-dimensional features and provides a linear projection matrix, and then both the between-class and the within-class scatter matrices are redefined by the classification probability. Secondly, through maximizing the between-class scatter and minimizing the within-class scatter simultaneously, a projection matrix can be computed and the high-dimensional data are projected to the low-dimensional feature space. The experimental results on gait database demonstrate the effectiveness of the proposed method.
01 Jul 2016
TL;DR: Results show that the human-robot interactive interface based on identity recognition can push corresponding needs to different users effectively, which provides a new way for the collaboration between human and service robot.
Abstract: To improve the interactive experience in cooperation between users and service robots, a human-robot interactive interface based on identity recognition has been designed. Through the method of face recognition to determine the identity of users, the robot provides personalized service to the users by the information of them. Furthermore, some experiments have been done in the indoor environment, the results show that the human-robot interactive interface based on identity recognition can push corresponding needs to different users effectively, which provides a new way for the collaboration between human and service robot.
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"Gait Recognition Based Online Perso..." refers methods in this paper
...Then, using the Gram-Schmidt orthonormalization [21], we first construct the orthonormal basis set γns that spans both Ω and Ψ and x− y....
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TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
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TL;DR: A new supervised dimensionality reduction algorithm called marginal Fisher analysis is proposed in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizing the interclass separability.
Abstract: A large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called marginal Fisher analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional linear discriminant analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions
2,751 citations