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Research on Artificial Intelligence and Face Recognition
- 1 Zhejiang University of Technology, Hangzhou, Zhejiang Province, China, 310000
* Author to whom correspondence should be addressed.
Abstract
With the rapid progress of artificial intelligence technologies, face recognition has emerged as a critical domain within biometrics. The primary objective of face recognition technology is to facilitate rapid identification and authentication of individuals by analyzing facial image features. This technology has been widely applied in various sectors, including security surveillance, financial transactions, education, healthcare, and more. This paper presents a comprehensive overview and analysis of contemporary face recognition methodologies, including geometric feature extraction, the combination of Wavelet transform and Principal Component Analysis (PCA) in artificial neural networks, elastic graph matching, Linear Discriminant Hashing (LHD), and Support Vector Machines (SVM). Through a comparative evaluation of these methods, this study clarifies their respective advantages and limitations. The findings indicate that face recognition technology basically consists of essential components such as face detection, image processing, feature extraction, feature comparison, and decision-making for recognition. Although there are similarities in the general workflow among different methodologies, each approach optimizes specific aspects to varying extents. As a result, every method has its own distinct strengths and weaknesses. Therefore, choosing the most suitable technique for practical applications usually depends on specific requirements and contextual conditions. X
Keywords
Artificial intelligence, face recognition, elastic graph matching, Linear Discriminant Hashing (LHD), Support Vector Machines (SVM).
[1]. Fu Feng, Wang Hanwei. Practical Inspection and Theoretical Response to Face Recognition as Criminal Evidence -- Taking Criminal Judgment Documents from 2013 to 2022 as research Samples [J]. Evidence Science, 24,32(04):451-467.
[2]. Huang Chunmei. Third Party payment System based on Face recognition [D]. Yangzhou University,2019.
[3]. Ma Shishun. Civil face recognition security risk prevention and control research [D]. The Chinese people's public security university, 2023. The DOI: 10.27634 /, dc nki. Gzrgu. 2023.000016.
[4]. LIANG Yuan-Kai. Characteristics and Direction of face database development [J]. Computer and Networks,2021,47(04):64-67.
[5]. Zhang Jun, He Xin, Li Jiegu. Face recognition method based on facial geometric feature point extraction [J]. Infrared and Laser Engineering,1999,(04):40-43.
[6]. HU L. Research on PCA face recognition method based on wavelet transform and artificial neural network [D]. Soochow University,2002.
[7]. ZHANG Hai-long. Research on elastic Tractograph matching algorithm for face recognition [D]. Northeastern University,2008.
[8]. DU Cheng, SU Guangda, Lin Xinggang, et al. Face recognition method based on improved line segment Hausdorff distance [J]. Photoelectron · Laser,2005,(01):89-93.
[9]. WANG Yixin. Research on face detection and recognition system based on Support Vector Machine technology [D]. National University of Defense Technology,2003.
[10]. Zhao. Theory of legal regulation of the face recognition [D]. Shanxi university of finance and economics, 2023, DOI: 10.27283 /, dc nki. GSXCC. 2023.000601.
Cite this article
Yao,X. (2024). Research on Artificial Intelligence and Face Recognition. Applied and Computational Engineering,112,79-84.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
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