Model-Based Human Gait Recognition Via Deterministic Learning

Advances in Vision-Based Gait Recognition: From Handcrafted to Deep Learning

Identifying people’s identity by using behavioral biometrics has attracted many researchers’ attention in the biometrics industry. Gait is a behavioral trait, whereby an individual is identified based on their walking style. Over the years, gait recognition has been performed by using handcrafted approaches. However, due to several covariates’ effects, the competence of the approach has been compromised. Deep learning is an emerging algorithm in the biometrics field, which has the capability to tackle the covariates and produce highly accurate results. In this paper, a comprehensive overview of the existing deep learning-based gait recognition approach is presented. In addition, a summary of the performance of the approach on different gait datasets is provided.

Human gait analysis for osteoarthritis prediction: a framework of deep learning and kernel extreme learning machine

Human gait analysis is a novel topic in the field of computer vision with many famous applications like prediction of osteoarthritis and patient surveillance. In this application, the abnormal behavior like problems in walking style is detected of suspected patients. The suspected behavior means assessments in terms of knee joints and any other symptoms that directly affected patients’ walking style. Human gait analysis carries substantial importance in the medical domain, but the variability in patients’ clothes, viewing angle, and carrying conditions, may severely affect the performance of a system. Several deep learning techniques, specifically focusing on efficient feature selection, have been recently proposed for this purpose, unfortunately, their accuracy is rather constrained. To address this disparity, we propose an aggregation of robust deep learning features in Kernel Extreme Learning Machine. The proposed framework consists of a series of steps. First, two pre-trained Convolutional Neural Network models are retrained on public gait datasets using transfer learning, and features are extracted from the fully connected layers. Second, the most discriminant features are selected using a novel probabilistic approach named Euclidean Norm and Geometric Mean Maximization along with Conditional Entropy. Third, the aggregation of the robust features is performed using Canonical Correlation Analysis, and the aggregated features are subjected to various classifiers for final recognition. The evaluation of the proposed scheme is performed on a publicly available gait image dataset CASIA B. We demonstrate that the proposed feature aggregation methodology, once used with the Kernel Extreme Learning Machine, achieves accuracy beyond 96%, and outperforms the existing works and several other widely adopted classifiers.

A Machine Learning Method with Threshold Based Parallel Feature Fusion and Feature Selection for Automated Gait Recognition

Gait is a vital biometric process for human identification in the domain of machine learning. In this article, a new method is implemented for human gait recognition based on accurate segmentation and multi-level features extraction. Four major steps are performed including: a) enhancement of motion region in frame by the implementation of linear transformation with HSI color space; b) Region of Interest (ROI) detection based on parallel implementation of optical flow and background subtraction; c) shape and geometric features extraction and parallel fusion; d) Multi-class support vector machine (MSVM) utilization for recognition. The presented approach reduces error rate and increases the CCR. Extensive experiments are done on three data sets namely CASIA-A, CASIA-B and CASIA-C which present different variations in clothing and carrying conditions. The proposed method achieved maximum recognition results of 98.6% on CASIA-A, 93.5% on CASIA-B and 97.3% on CASIA-C, respectively.

Class Energy Image analysis for video sensor-based gait recognition: a review

Gait is a unique perceptible biometric feature at larger distances, and the gait representation approach plays a key role in a video sensor-based gait recognition system. Class Energy Image is one of the most important gait representation methods based on appearance, which has received lots of attentions. In this paper, we reviewed the expressions and meanings of various Class Energy Image approaches, and analyzed the information in the Class Energy Images. Furthermore, the effectiveness and robustness of these approaches were compared on the benchmark gait databases. We outlined the research challenges and provided promising future directions for the field. To the best of our knowledge, this is the first review that focuses on Class Energy Image. It can provide a useful reference in the literature of video sensor-based gait representation approach.