Towards Scalable View-Invariant Gait Recognition: Multilinear Analysis for Gait. In: Kanade T, Jain A, Ratha NK, editors. Audio- and Video-Based Biometric Person Authentication. Berlin: Springer Berlin Heidelberg; 2005. pp. 395-405. [DOI: 10.1007/11527923_41]

Free-view gait recognition

Human gait has been shown to be an effective biometric measure for person identification at a distance. On the other hand, changes in the view angle pose a major challenge for gait recognition as human gait silhouettes are usually different from different view angles. Traditionally, such a multi-view gait recognition problem can be tackled by View Transformation Model (VTM) which transforms gait features from multiple gallery views to the probe view so as to evaluate the gait similarity. In the real-world environment, however, gait sequences may be captured from an uncontrolled scene and the view angle is often unknown, dynamically changing, or does not belong to any predefined views (thus VTM becomes inapplicable). To address this free-view gait recognition problem, we propose an innovative view-adaptive mapping (VAM) approach. The VAM employs a novel walking trajectory fitting (WTF) to estimate the view angles of a gait sequence, and a joint gait manifold (JGM) to find the optimal manifold between the probe data and relevant gallery data for gait similarity evaluation. Additionally, a RankSVM-based algorithm is developed to supplement the gallery data for subjects whose gallery features are only available in predefined views. Extensive experiments on both indoor and outdoor datasets demonstrate that the VAM outperforms several reference methods remarkably in free-view gait recognition.

Development of vision based multiview gait recognition system with MMUGait database

This paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based gait recognition system with joint detection approach that performs well under different walking trajectories and covariate factors, which include self-occluded or external occluded silhouettes. In the proposed system, the process begins by enhancing the human silhouette to remove the artifacts. Next, the width and height of the body are obtained. Subsequently, the joint angular trajectories are determined once the body joints are automatically detected. Lastly, crotch height and step-size of the walking subject are determined. The extracted features are smoothened by Gaussian filter to eliminate the effect of outliers. The extracted features are normalized with linear scaling, which is followed by feature selection prior to the classification process. The classification experiments carried out on MMUGait database were benchmarked against the SOTON Small DB from University of Southampton. Results showed correct classification rate above 90% for all the databases. The proposed approach is found to outperform other approaches on SOTON Small DB in most cases.

MPCA: Multilinear Principal Component Analysis of Tensor Objects

This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objects of interest in many computer vision and pattern recognition applications, such as 2-D/3-D images and video sequences are naturally described as tensors or multilinear arrays. The proposed framework performs feature extraction by determining a multilinear projection that captures most of the original tensorial input variation. The solution is iterative in nature and it proceeds by decomposing the original problem to a series of multiple projection subproblems. As part of this work, methods for subspace dimensionality determination are proposed and analyzed. It is shown that the MPCA framework discussed in this work supplants existing heterogeneous solutions such as the classical principal component analysis (PCA) and its 2-D variant (2-D PCA). Finally, a tensor object recognition system is proposed with the introduction of a discriminative tensor feature selection mechanism and a novel classification strategy, and applied to the problem of gait recognition. Results presented here indicate MPCA's utility as a feature extraction tool. It is shown that even without a fully optimized design, an MPCA-based gait recognition module achieves highly competitive performance and compares favorably to the state-of-the-art gait recognizers.