Human Skeleton Model Based Dynamic Features for Walking Speed Invariant Gait Recognition

Abstract

Humans are able to recognize small number of people they know well by the way they walk. This ability represents basic motivation for using human gait as the means for biometric identification. Such biometrics can be captured at public places from a distance without subject's collaboration, awareness, and even consent. Although current approaches give encouraging results, we are still far from effective use in real-life applications. In general, methods set various constraints to circumvent the influence of covariate factors like changes of walking speed, view, clothing, footwear, and object carrying, that have negative impact on recognition performance. In this paper we propose a skeleton model based gait recognition system focusing on modelling gait dynamics and eliminating the influence of subjects appearance on recognition. Furthermore, we tackle the problem of walking speed variation and propose space transformation and feature fusion that mitigates its influence on recognition performance. With the evaluation on OU-ISIR gait dataset, we demonstrate state of the art performance of proposed methods.

Attribute-based learning for gait recognition using spatio-temporal interest points

“…Bobick and Johnson [4,10] used activity-specific static body parameters for gait recognition without directly analyzing dynamics of gait patterns. Kovac and Peer [2] proposed a skeleton model-based gait recognition method by modeling gait dynamics and eliminating influences of subjects' appearances on recognition. Tafazzoli and Safabakhsh [8] applied active contour models and the Hough transform to model movements of articulated parts of the human body.…”

“…For example, BenAbdelkader et al [21] proposed an EigenGait method using image self-similarity plots. Chai et al [2] introduced a Perceptual Shape Descriptor technique for recognizing gaits. Tan et al [25] used eight kinds of projective features to describe human gait and PCA was applied for gait feature dimension reduction.…”

“…A large number of gait recognition methods have been developed and published, which can be roughly divided into two categories: 1) model-based methods [1][2][3][4][5][6][7][8][9][10]; 2) modelfree methods [11][12][13][14][15][16][17][18][19][20][21][22]. These methods require a pre-processing to extract gait appearances such as shape contours, silhouettes, skeletons, and body joints for further gait analysis.…”

Assessing similarity models for human‐motion retrieval applications

“…To describe characteristic aspects of motion data, various kinds of motion features can be extracted from the raw 3D joint coordinates, such as joint angles (rotations) , distances between joints , relative joint velocity and acceleration , and their combinations . The extracted features provide a necessary abstraction of motion data by emphasizing important motion components while suppressing everything insignificant to our interest.…”

“…As soon as appropriate features are chosen and ex‐ tracted, it is essential to define the way of their comparison. The extracted features can be compared on the basis of nearest‐neighbor search (e.g., provided by index structures) , term frequency – inverse document frequency (TF‐IDF) , machine learning (e.g., provided by support vector machines (SVMs) or artificial neural networks ), and probabilistic methods (e.g., provided by Markov models or Bayesian networks ). The TF‐IDF and nearest‐neighbor approaches return a list of the most similar motions with respect to a query motion example as the comparison output, while machine learning and probabilistic methods return a set of classes to which the query example is classified.…”