Studies advanced in face recognition

Research Article
Open access

Studies advanced in face recognition

Haoyu Wang 1*
  • 1 Northeast Forest University, Haerbin, Heilongjiang Province, 150040, China    
  • *corresponding author 2020213504@nefu.edu.cn
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230946
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Face recognition has always been a hot topic in the field of computer vision research. Its purpose is to enable computers to have the ability to recognize people through face images like humans. The early face recognition technology was mainly based on artificial features, mainly including five parts: face image acquisition, face image detection, face image preprocessing, face image feature extraction and matching recognition. Thanks to the rapid development of convolutional neural networks, face recognition technology based on deep learning has gradually become the mainstream method. Focusing on the above two categories of frameworks, in this paper, we introduce representative algorithms for face recognition, including their design ideas, basic processes and key steps. We also discuss existing problems in the field of face recognition and predict future directions for this topic.

Keywords:

Face Recognition, Deep Learning, DeepID.

Wang,H. (2023). Studies advanced in face recognition. Applied and Computational Engineering,6,738-744.
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References

[1]. Turk M, Pentland A. Eigenfaces for recognition[J]. Journal of cognitive neuroscience, 1991, 3(1): 71-86.

[2]. Turk M A, Pentland A P. Face recognition using eigenfaces[C]//Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference on. IEEE, 1991: 586-591.

[3]. Belhumeur P N, Hespanha J P, Kriegman D. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1997, 19(7): 711-720.

[4]. M. D. Kelly, “Visual identification of people by computer.,” tech. rep., STANFORD UNIV CALIF DEPT OF COMPUTER SCIENCE, 1970.

[5]. T. KANADE, “Picture processing by computer complex and recognition of human faces,” PhD Thesis, Kyoto University, 1973.

[6]. K. Delac and M. Grgic, “A survey of biometric recognition methods,” in 46th International Symposium Electronics in Marine, vol. 46, pp. 16– 18, 2004.

[7]. U. Park, Y. Tong, and A. K. Jain, “Age-invariant face recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 5, pp. 947–954, 2010.

[8]. Z. Li, U. Park, and A. K. Jain, “A discriminative model for age invariant face recognition,” IEEE transactions on information forensics and security, vol. 6, no. 3, pp. 1028–1037, 2011.

[9]. Brunelli R, PoggioT. Face Recognition: Features Versus Templates [ J]. IEEE T rans on Pattern Analysis and M achine Inteligence , 1993, 15 (10):10421052

[10]. M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of cognitive neuroscience, vol. 3, no. 1, pp. 71–86, 1991.

[11]. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711–720, 1997.

Cite this article

Wang,H. (2023). Studies advanced in face recognition. Applied and Computational Engineering,6,738-744.

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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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Turk M, Pentland A. Eigenfaces for recognition[J]. Journal of cognitive neuroscience, 1991, 3(1): 71-86.

[2]. Turk M A, Pentland A P. Face recognition using eigenfaces[C]//Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference on. IEEE, 1991: 586-591.

[3]. Belhumeur P N, Hespanha J P, Kriegman D. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1997, 19(7): 711-720.

[4]. M. D. Kelly, “Visual identification of people by computer.,” tech. rep., STANFORD UNIV CALIF DEPT OF COMPUTER SCIENCE, 1970.

[5]. T. KANADE, “Picture processing by computer complex and recognition of human faces,” PhD Thesis, Kyoto University, 1973.

[6]. K. Delac and M. Grgic, “A survey of biometric recognition methods,” in 46th International Symposium Electronics in Marine, vol. 46, pp. 16– 18, 2004.

[7]. U. Park, Y. Tong, and A. K. Jain, “Age-invariant face recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 5, pp. 947–954, 2010.

[8]. Z. Li, U. Park, and A. K. Jain, “A discriminative model for age invariant face recognition,” IEEE transactions on information forensics and security, vol. 6, no. 3, pp. 1028–1037, 2011.

[9]. Brunelli R, PoggioT. Face Recognition: Features Versus Templates [ J]. IEEE T rans on Pattern Analysis and M achine Inteligence , 1993, 15 (10):10421052

[10]. M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of cognitive neuroscience, vol. 3, no. 1, pp. 71–86, 1991.

[11]. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711–720, 1997.

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