Gait recognition based on model-based methods and deep belief networks

Benouis, Mohamed; Sarwat, Mohamed; Tlemsani, Redouane; Mostefai, L.

doi:10.1504/ijbm.2016.082598

Cited by 11 publications

(6 citation statements)

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“…DBNs are better than traditional shallow networks because they can learn more abstract and complex representations of data [ 110 ]. This may be helpful for gait recognition, where it can be hard to pick up on small differences between people’s steps.…”

Section: Taxonomymentioning

confidence: 99%

“…However, DBNs require more data and computational resources for training than shallow networks, and they may also suffer from issues such as vanishing gradients during training. Many DBNs are utilized for person identification using gait [ 110 , 111 ]. The research presented in [ 110 ] focused on extracting fitting body parameters and shape features from the silhouette.…”

Section: Taxonomymentioning

confidence: 99%

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Person Recognition Based on Deep Gait: A Survey

Khaliluzzaman

Uddin

Deb

et al. 2023

Sensors

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Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future.

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Section: Taxonomymentioning

confidence: 99%

Section: Taxonomymentioning

confidence: 99%

Person Recognition Based on Deep Gait: A Survey

Khaliluzzaman

Uddin

Deb

et al. 2023

Sensors

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“…The weights and biases of the units define a probability distribution over the joint states of the visible and hidden units. DBNs have been used for gait recognition in [90] and [25]. In [90], fitting, body parameters, and shape features were extracted from gait silhouettes.…”

Section: Deep Belief Networkmentioning

confidence: 99%

“…DBNs have been used for gait recognition in [90] and [25]. In [90], fitting, body parameters, and shape features were extracted from gait silhouettes. DBNs were then used to learn from these features, thus extracting more discriminative features.…”

Section: Deep Belief Networkmentioning

confidence: 99%

Deep Gait Recognition: A Survey

Sepas-Moghaddam¹,

Etemad²

2021

Preprint

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Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in the field and have fostered real-world applications. In this paper, we present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning, and cover broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. We first review the commonly used gait datasets along with the principles designed for evaluating them. We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize the research landscape and literature in this area. Following our proposed taxonomy, a comprehensive survey of gait recognition methods using deep learning is presented with discussions on their performances, characteristics, advantages, and limitations. We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.

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“…After that, several works have been developed where were using both the model free based and model based to extract the information's that describe the manner of human walk [18][19][20]. I. Rida, N. Almaadeed, and S. Almaadeed [21] give a comprehensive discussion about the state of the art in vision-based gait recognition up to 2018.…”

Section: Introductionmentioning

confidence: 99%

An Improved Behavioral Biometric System based on Gait and ECG signals

Guelta¹,

Tlemsani²,

Chouraqui³

et al. 2019

IJIES

Self Cite

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This paper presents multi-modal biometric authentication approach using gait and electrocardiogram (ECG) signals, which can diminish the drawback of unimodal biometric approach as well as to improve authentication system performance. In acquisition phase, data sets are collected from three different databases, ECG-ID, MIT-BIH Arrhythmia database and UCI Machine Learning Repository (Gait). In Feature extraction phase of both signals (ECG and Gait) is performed by using 1D-local binary pattern. Features are obtained by merging two modalities as one feature. In classification approach, three classifiers are developed to classify subjects. K-nearest neighbour (KNN), relying on Euclidean distance, PNN (Probabilistic Neural Network), RBF (Radial Basis Function) and Support Vector Machine (SVM), relying on One-against-all (OAA). The proposed multimodal system has been tested over 18 subjects, and its identification accuracy was about 100%. Our result demonstrate that our approach outperforms rather than unimodal biometric system in terms of Correct Recognition Rate, Equal Error Rate, False Acceptance Rate and False Reject Rate.

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Gait recognition based on model-based methods and deep belief networks

Cited by 11 publications

References 0 publications

Person Recognition Based on Deep Gait: A Survey

Person Recognition Based on Deep Gait: A Survey

Deep Gait Recognition: A Survey

An Improved Behavioral Biometric System based on Gait and ECG signals

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