Human Gait Recognition Using Bag of Words Feature Representation Method

Bayat, Nasrin; Rastegari, Elham; Li, Qifeng

doi:10.48550/arxiv.2203.13317

Cited by 2 publications

(2 citation statements)

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“…Multiple studies and investigations have shown that every individual has a distinct muscular-skeletal framework, making it possible to identify individuals based on their gait [8]. Gait recognition has several advantages over other biometric identifiers [9]. First, gait can be recorded from a distance without the participant's cooperation [10].…”

Section: Introductionmentioning

confidence: 99%

Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

Hanif,

Mughal,

Khan

et al. 2024

CMC

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The demand for a non-contact biometric approach for candidate identification has grown over the past ten years. Based on the most important biometric application, human gait analysis is a significant research topic in computer vision. Researchers have paid a lot of attention to gait recognition, specifically the identification of people based on their walking patterns, due to its potential to correctly identify people far away. Gait recognition systems have been used in a variety of applications, including security, medical examinations, identity management, and access control. These systems require a complex combination of technical, operational, and definitional considerations. The employment of gait recognition techniques and technologies has produced a number of beneficial and wellliked applications. This work proposes a novel deep learning-based framework for human gait classification in video sequences. This framework's main challenge is improving the accuracy of accuracy gait classification under varying conditions, such as carrying a bag and changing clothes. The proposed method's first step is selecting two pretrained deep learning models and training from scratch using deep transfer learning. Next, deep models have been trained using static hyperparameters; however, the learning rate is calculated using the particle swarm optimization (PSO) algorithm. Then, the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization algorithm. This algorithm chooses the best features, combined in a novel correlationbased fusion technique. Finally, the fused best features are categorized using medium, bi-layer, and tri-layered neural networks. On the publicly accessible dataset known as the CASIA-B dataset, the experimental process of the suggested technique was carried out, and an improved accuracy of 94.14% was achieved. The achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this work.

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Section: Introductionmentioning

confidence: 99%

Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

Hanif,

Mughal,

Khan

et al. 2024

CMC

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“…Recent advances in deep neural networks (DNN) [1]- [4] and access to very large datasets with million annotated data especially for computer vision applications have led to state-of-the-art results in many problem domains such as object detection and scene classification [5]- [7]. For example, Faster-RCNN network [8] achieved the impressive results in recognition and localization of objects in natural scenes or GoogleNet [9] reached approximately to the human performance in classification of the ImageNet database [10].…”

Section: Introductionmentioning

confidence: 99%

Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans

Ghasemi¹,

Mottaghian²,

Bayat³

2023

Preprint

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Object recognition systems are usually trained and evaluated on high resolution images. However, in real world applications, it is common that the images have low resolutions or have small sizes. In this study, we first track the performance of the state-of-the-art deep object recognition network, Faster-RCNN, as a function of image resolution. The results reveals negative effects of low resolution images on recognition performance. They also show that different spatial frequencies convey different information about the objects in recognition process. It means multi-resolution recognition system can provides better insight into optimal selection of features that results in better recognition of objects. This is similar to the mechanisms of the human visual systems that are able to implement multi-scale representation of a visual scene simultaneously. Then, we propose a multi-resolution object recognition framework rather than a single-resolution network. The proposed framework is evaluated on the PASCAL VOC2007 database. The experimental results show the performance of our adapted multi-resolution Faster-RCNN framework outperforms the single-resolution Faster-RCNN on input images with various resolutions with an increase in the mean Average Precision (mAP) of 9.14% across all resolutions and 1.2% on the full-spectrum images. Furthermore, the proposed model yields robustness of the performance over a wide range of spatial frequencies.

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Human Gait Recognition Using Bag of Words Feature Representation Method

Cited by 2 publications

References 0 publications

Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

Improving Performance of Object Detection using the Mechanisms of Visual Recognition in Humans

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