Remote Identity Verification Using Gait Analysis and Face Recognition

Si, Wen; Zhang, Jing; Li, Yudong; Tan, Wenan; Shao, Yifan; Yang, Gelan

doi:10.1155/2020/8815461

“…The study reported signi cant improvements in recognition accuracy compared to conventional methods, highlighting the synergies between gait patterns and anthropometric data. Researchers extended this line of research, exploring the complementary nature of anthropometric data and gait features [17]. These studies collectively underscore the potential of anthropometric data integration to advance the capabilities of gait recognition systems.…”

Section: Integration Of Anthropometric Datamentioning

confidence: 92%

GaitHF: Height Fused Gait Recognition

Zhu,

Sun,

Cheng

et al. 2024

Preprint

0

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Gait recognition, an emerging field at the intersection of computer vision and biometrics, has garnered significant attention for its potential applications in surveillance, security, and healthcare. In this paper, we present a novel method that combines appearance-based gait recognition with human height data. The proposed approach aims to enhance the accuracy and robustness of gait recognition systems by incorporating complementary features derived from both gait patterns and human height. We believe that incorporating height information can offer additional discriminative power to gait recognition models, enabling them to better distinguish individuals in various scenarios. Many gait recognition convolutional neural networks using deep learning methods have made good data progress in recent years, so we also adopt this approach, e.g., deep learning methods such as (CNN) and Recurrent Neural Networks (RNN), which can automatically learn hierarchical representations of gait and height features to capture intricate patterns and relationships. Our experiments involve a comprehensive analysis using benchmark gait datasets, demonstrating the effectiveness of the proposed approach in comparison to traditional gait recognition methods. The results highlight the potential of leveraging human height information to enhance the overall performance of gait recognition systems. Our experimental data show that the results achieved by many appearance-based gait recognition models on the CASIA-B and OU-MVLP datasets progress in most conditions after using our proposed new approach, which are eye-catching in that the average accuracy improves by 1.875% and 6% on BG and CL of CASIA-B, respectively, and the average accuracy improves on the large dataset OU-MVLP is also improved by 1.35%.

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“…The study reported significant improvements in recognition accuracy compared to conventional methods, highlighting the synergies between gait patterns and anthropometric data. Researchers extended this line of research, exploring the complementary nature of anthropometric data and gait features [21]. These studies collectively underscore the potential of anthropometric data integration to advance the capabilities of gait recognition systems.…”

Section: Integration Of Anthropometric Datamentioning

confidence: 92%

GaitHF: Enhancing Appearance-Based Gait Recognition through Height Fused Images

Zhu,

Sun,

Cheng

et al. 2024

Preprint

0

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Gait recognition, an emerging field at the intersection of computer vision and biometrics, has garnered significant attention for its potential applications in surveillance, security, and healthcare. In this paper, we present a novel method that combines appearance-based gait recognition with human height data. The proposed approach aims to enhance the accuracy and robustness of gait recognition systems by incorporating complementary features derived from both gait patterns and human height. We believe that incorporating height information can offer additional discriminative power to gait recognition models, enabling them to better distinguish individuals in various scenarios. Many gait recognition convolutional neural networks using deep learning methods have made good data progress in recent years, so we also adopt this approach, e.g., deep learning methods such as (CNN) and Recurrent Neural Networks (RNN), which can automatically learn hierarchical representations of gait and height features to capture intricate patterns and relationships. Our experiments involve a comprehensive analysis using benchmark gait datasets, demonstrating the effectiveness of the proposed approach in comparison to traditional gait recognition methods. The results highlight the potential of leveraging human height information to enhance the overall performance of gait recognition systems. Our experimental data show that the results achieved by many appearance-based gait recognition models on the CASIA-B and OU-MVLP datasets progress in most conditions after using our proposed new approach, which are eye-catching in that the average accuracy improves by 1.875% and 6% on BG and CL of CASIA-B, respectively, and the average accuracy improves on the large dataset OU-MVLP is also improved by 1.35%. Overall, our work focuses on analyzing and recognizing gait images, contributing to gait recognition. The source code and datasets can be accessed at https://github.com/ReinerBRO/GaitHF.

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“…Huan et al [21] makes use of acceleration sensors in smartphones and explores a way to analyze the gait of a person. Si et al [22]. have made use of remote sensing system for security area monitoring which collects signals from people walking towards the system where the data of their walking and face is extracted and is processed to recognize the person.…”

Section: Relevant Workmentioning

confidence: 99%

Gait Recognition Using Convolutional Neural Network

Sheth¹,

Sharath²,

Reddy³

et al. 2023

Int. J. Onl. Eng.

2

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Biometrics are the body measurements and calculations related to individuals. Biometrics validation is used as a form of identification of individual. Gait recognition system is one of the most advanced technology that people have been working on for a while now that takes center stage in the field of biometrics. Compared to the other types of existing systems of biometric recognition such as fingerprint detection, iris-scanning systems etc., Gait Recognition system ensures no human intervention. This paper focuses on recognition based on a person’s gait. Every person has a distinct gait pattern that is unique to every other person. To train the model CASIA-B dataset has been used. The dataset includes 124 subjects where each sample has undergone Gait Energy Image extraction. Samples with clothing and baggage have been included which changes the silhouette of the person. Therefore, the model has been trained for a wider application where people wear different type of clothing and carry-ons. A Convolutional Neural Network consisting of 8 layers has been trained which performs well on both samples of dataset and an accuracy of 95.45% was obtained on dataset not involving layers of clothing and accuracy of 91.80% was obtained for the sample with clothing and baggage.

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Remote Identity Verification Using Gait Analysis and Face Recognition

Cited by 8 publications

References 42 publications

GaitHF: Height Fused Gait Recognition

GaitHF: Height Fused Gait Recognition

GaitHF: Enhancing Appearance-Based Gait Recognition through Height Fused Images

Gait Recognition Using Convolutional Neural Network

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