The Application of Artificial Intelligence and Machine Learning in Face Recognition Technology

In the wake of the swift development of artificial intelligence (AI) and machine learning (ML) technologies, face recognition technology has emerged as a prominent research focus within the realm of biometrics. This paper delves into the most recent advancements of AI and ML algorithms with regard to enhancing the accuracy and speed of face recognition. To begin with, a comprehensive review of the development of face recognition technology is conducted. It traces the progression from traditional methods to the application of deep learning technology, while also summarizing the merits and limitations of the existing technology. Subsequently, the key technologies used in this paper are elaborated upon in meticulous These encompass the convolutional neural network (CNN), deep learning feature extraction, transfer learning, and the attention mechanism in face recognition, among others. These markedly augment the model's processing capabilities when dealing with complex scenes, varying lighting conditions, and occlusion situations. Furthermore, this paper undertakes an exploration of privacy protection and ethical concerns, It puts forward strategies aimed at bolstering data protection and privacy security without compromising the identification performance. Finally, the principal findings of this study are encapsulated, and future research directions are outlined. These include the further optimization of algorithms to curtail the consumption of computing resources, the development of more efficient data enhancement techniques to enhance model generalization, and exploration of a broader range of application scenarios, such as intelligent security, personalized services, and accessibility assistance systems. This study not only provides theoretical underpinning and practical guidance for the development of face recognition technology but also paves the way for promoting the extensive application of AI technology in social life.

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Publication Details

Journal
Applied and Computational Engineering
Published
2024-12-19
DOI
https://doi.org/10.54254/2755-2721/2025.18479
Citations
3
Primary Topic
Face recognition and analysis
Type
article
Field-Weighted Citation Impact
0.75
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article

The Application of Artificial Intelligence and Machine Learning in Face Recognition Technology

Zhonghao Li
3 citations
Applied and Computational Engineering
Face recognition and analysis
0.75
article

The Application of Artificial Intelligence and Machine Learning in Face Recognition Technology

Zhonghao Li
article en
3 citations

Abstract

In the wake of the swift development of artificial intelligence (AI) and machine learning (ML) technologies, face recognition technology has emerged as a prominent research focus within the realm of biometrics. This paper delves into the most recent advancements of AI and ML algorithms with regard to enhancing the accuracy and speed of face recognition. To begin with, a comprehensive review of the development of face recognition technology is conducted. It traces the progression from traditional methods to the application of deep learning technology, while also summarizing the merits and limitations of the existing technology. Subsequently, the key technologies used in this paper are elaborated upon in meticulous These encompass the convolutional neural network (CNN), deep learning feature extraction, transfer learning, and the attention mechanism in face recognition, among others. These markedly augment the model's processing capabilities when dealing with complex scenes, varying lighting conditions, and occlusion situations. Furthermore, this paper undertakes an exploration of privacy protection and ethical concerns, It puts forward strategies aimed at bolstering data protection and privacy security without compromising the identification performance. Finally, the principal findings of this study are encapsulated, and future research directions are outlined. These include the further optimization of algorithms to curtail the consumption of computing resources, the development of more efficient data enhancement techniques to enhance model generalization, and exploration of a broader range of application scenarios, such as intelligent security, personalized services, and accessibility assistance systems. This study not only provides theoretical underpinning and practical guidance for the development of face recognition technology but also paves the way for promoting the extensive application of AI technology in social life.

Applied and Computational EngineeringVol. 115(1)
Beijing University of Posts and Telecommunications (CN), Century College (US)
Partnerships for the goals
Openalex Percentile: Top 26%
Face recognition and analysis
0.75
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