Abstract
Gait is a unique non-invasive biometric form that can be utilized to effectively recognize persons, even when they prove to be uncooperative. Computer-aided gait recognition systems usually use image sequences without considering covariates like clothing and possessions of carrier bags whilst on the move. Similarly, in gait recognition, there may exist unknown covariate conditions that may affect the training and testing conditions for a given individual. Consequently, common techniques for gait recognition and measurement require a degree of intervention leading to the introduction of unknown covariate conditions, and hence this significantly limits the practical use of the present gait recognition and analysis systems. To overcome these key issues, we propose a method of gait analysis accounting for both known and unknown covariate conditions. For this purpose, we propose two methods, i.e., a Convolutional Neural Network (CNN) based gait recognition and a discriminative features-based classification method for unknown covariate conditions. The first method can handle known covariate conditions efficiently while the second method focuses on identifying and selecting unique covariate invariant features from the gallery and probe sequences. The feature set utilized here includes Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and Haralick texture features. Furthermore, we utilize the Fisher Linear Discriminant Analysis for dimensionality reduction and selecting the most discriminant features. Three classifiers, namely Random Forest, Support Vector Machine (SVM), and Multilayer Perceptron are used for gait recognition under strict unknown covariate conditions. We evaluated our results using CASIA and OUR-ISIR datasets for both clothing and speed variations. As a result, we report that on average we obtain an accuracy of 90.32% for the CASIA dataset with unknown covariates and similarly performed excellently on the ISIR dataset. Therefore, our proposed method outperforms existing methods for gait recognition under known and unknown covariate conditions.
Highlights
Gait is a biometric trait that depicts and measures how people move
The results prove that our method performs better than existing work and the important conclusion can be made that simple Gait Energy Image (GEI) with deep learning is enough to handle noncovariate conditions
2) RESULTS FOR GAIT RECOGNITION FOR UNKNOWN COVARIATE CONDITIONS This section presents results for covariate conditions where it is strictly maintained that the gallery and probe sets are not overlapped
Summary
Gait is a biometric trait that depicts and measures how people move. Gait analysis has been successfully used in different domains, including biometrics and posture analysis for healthcare applications. It has been used in human psychology where gait analysis using point lights employed for recognition of emotional patterns. Borrowing from this, computer vision-based approaches have used motion analysis and human movement modeling for person identification [2]. In the early days of gait recognition, the focus was to identify and classify the different movement patterns such as walking, jogging, and climbing. The focus shifted towards human identification and has become an active area of research.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
