Deep learning pipelines for recognition of gait biometrics with covariates: a comprehensive review

Parashar, Anubha; Parashar, Apoorva; Ding, Weiping; Shekhawat, Rajveer Singh; Rida, Imad

doi:10.1007/s10462-022-10365-4

Cited by 19 publications

(2 citation statements)

References 142 publications

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“…We sourced data from social media platforms such as Facebook [16]. Prior to training, we subjected the dataset to extensive preprocessing [17]. This involved tasks such as lowercasing, punctuation removal, and tokenization.…”

Section: Data Pre-processingmentioning

confidence: 99%

Machine Learning Approach to Determine the Impact of Hate Speech Based on Public Opinions

2024

IJCATR

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In an era dominated by online communication, the prevalence of hate speech has emerged as a significant societal concern. This paper presents a comprehensive study utilizing machine learning techniques to assess the detrimental effects of hate speech on public opinions. Utilizing a varied dataset encompassing public sentiments, we utilize advanced machine learning algorithms to categorize the adverse effects of hate speech. This study employs a combination of quantitative, qualitative, and experimental research methodologies. The experimentation phase involved training and testing the models using Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). The resulting model demonstrates high accuracy and precision, with LR achieving (0.96, 0.97), D T showing (0.99, 100), and RF exhibiting (100, 100). Our findings reveal compelling insights into the negative impact of hate s peech. By classifying the negative repercussions, this research not only advances our understanding of online discourse but also provides a valuable foundation for the development of strategies to combat hate speech and cultivate a more inclusive digital environment.

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Section: Data Pre-processingmentioning

confidence: 99%

Machine Learning Approach to Determine the Impact of Hate Speech Based on Public Opinions

2024

IJCATR

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“…Biometrics, a discipline dedicated to studying distinctive physical and behavioral traits that are challenging to imitate, encompasses a myriad of data types, including fingerprints, iris patterns, facial features, voice, gait, signature, EEG/ECG, palm, and more. These biometric data play a pivotal role in verification and person recognition across various facets of daily life and in academic research [1][2][3][4]. The global shift to remote activities during the pandemic prompted the evolution of diverse approaches for acquiring biometric data [5].…”

Section: Introductionmentioning

confidence: 99%

Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security

Salturk,

Kahraman

2024

Neural Comput & Applic

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The significant increase in online activities in the wake of recent global events has underlined the importance of biometric person authentication on digital platforms. Although many biometric devices may be used for precise biometric authentication, acquiring the necessary technology, such as 3D sensors or fingerprint scanners, can be prohibitively expensive and logistically challenging. Addressing the demands of online environments, where access to specialized hardware is limited, this paper introduces an innovative approach. In this work, by fusing static and dynamic signature data with facial data captured through regular computer cameras, a dataset of 1750 samples from 25 individuals is constructed. Deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional networks (TCN), are employed to craft a robust multi-classification model. This integration of various deep learning algorithms has demonstrated remarkable performance enhancements in biometric authentication. This research also underscores the potential of merging dynamic and static biometric features, derived from readily available sources, to yield a high-performance recognition framework. As online interactions continue to expand, the combination of various biometric modalities holds potential for enhancing the security and usability of virtual environments.

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Federated learning for biometric recognition: a survey

Guo,

Mu,

Liu

et al. 2024

Artif Intell Rev

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Recently, deep learning (DL) has achieved great success in biometric recognition. The application of DL has also led to a high demand for biometric data. However, as people attach more importance to privacy protection, biometric data have become increasingly difficult to obtain and access, leading to significant limitations in the development and application of DL-based biometric recognition. Federated learning (FL), a distributed learning technique with privacy protection, provides a potential solution to this problem. Several researchers have attempted to integrate FL into biometric recognition. These studies have shown that the introduction of FL not only solves the conflict between privacy and accessibility of biometric data but also improves the accuracy and generalizability of local recognition systems. Therefore, the combination of FL and biometric recognition techniques has become a new research hotspot. In this survey, we comprehensively review the latest advances regarding the application of FL to biometric recognition, biometric presentation attack detection and the related fields to provide new researchers with a quick and systematic overview of this emerging cross-disciplinary field. This paper also summarizes the future opportunities and challenges of this field. To our knowledge, this is the first survey that systematically organizes and analyses federated biometric recognition and related fields to provide suggestions and references for future research.

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Deep learning pipelines for recognition of gait biometrics with covariates: a comprehensive review

Cited by 19 publications

References 142 publications

Machine Learning Approach to Determine the Impact of Hate Speech Based on Public Opinions

Machine Learning Approach to Determine the Impact of Hate Speech Based on Public Opinions

Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security

Federated learning for biometric recognition: a survey

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