Contribution to the fusion of soft facial and body biometrics for remote people identification

Ghalleb, Asma El Kissi; Slamia, Riwa Ben; Amara, Najoua Essoukri Ben

doi:10.1109/atsip.2016.7523078

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…Apart from the complementary (hard-)biometric traits, soft-biometric traits such as age [223], height [224], [225], weight [226], gender [227], and particular body marks including tattoos [228] can also be included to boost overall performance. The combination of other softand hard-biometric traits with gait has mostly been done in the literature based on non-deep methods [229], [230], [231], [232], [233], [234], while multi-modal deep learning methods [235], [236], notably based on fusion [237], joint learning [208], and attention [238] networks, can also be adopted. Hence, we anticipate that research on deep multi-biometric recognition systems that include gait, gain popularity in the coming years.…”

Section: Multi-biometric Recognitionmentioning

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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Section: Multi-biometric Recognitionmentioning

confidence: 99%

Deep Gait Recognition: A Survey

Sepas-Moghaddam¹,

Etemad²

2021

Preprint

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“…Gait, a behavioral biometric, is a new area of research that looks at how people walk to find out important information about them [ 1 ]. Because of this, the process is used for a lot for things such as security, surveillance, law enforcement, health, sports, and identifying people [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…In psycho-physiological studies [ 7 , 22 ], it was found that a person’s sex can be guessed with 80% accuracy based on how they walk. It is also revealed that any person’s emotion [ 23 , 24 ], feelings, and body weight [ 12 ] can be identified using the gait feature [ 21 ]. Most of the time, the approaches used in gait recognition are an end-to-end model, which exclude the preprocessing steps.…”

Section: Introductionmentioning

confidence: 99%

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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Soft biometrics: a survey

Hassan

Izquierdo

Piatrik

2021

Multimed Tools Appl

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The field of biometrics research encompasses the need to associate an identity to an individual based on the persons physiological or behaviour traits. While the use of intrusive techniques such as retina scans and finger print identification has resulted in highly accurate systems, the scalability of such systems in real-world applications such as surveillance and border security has been limited. As a branch of biometrics research, the origin of soft biometrics could be traced back to need for non-intrusive solutions for extracting physiological traits of a person. Following high number of research outcomes reported in the literature on soft biometrics, this paper aims to consolidate the scope of soft biometrics research across four thematic schemes (i) a detailed review of soft biometrics research data sets, their annotation strategies and building a largest novel collection of soft traits; (ii) the assessment of metrics that affect the performance of soft biometrics system; (iii) a comparative analysis on feature and modality level fusion reported in the literature for enhancing the system performance; and (iv) a performance analysis of hybrid soft biometrics recognition system using multi-scale criterion. The paper also presents a detailed analysis on the global traits associated to person identity such as gender, age and ethnicity. The contribution of the paper is to provide a comprehensive review of scientific literature, identify open challenges and offer insights on new research directions in the filed.

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Contribution to the fusion of soft facial and body biometrics for remote people identification

Cited by 5 publications

References 29 publications

Deep Gait Recognition: A Survey

Deep Gait Recognition: A Survey

Person Recognition Based on Deep Gait: A Survey

Soft biometrics: a survey

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