Open Access Open Access  Restricted Access Subscription or Fee Access

Comparison of Machine Learning Classification Algorithms for Face Recognition Detection and Recognition: A Review

Gajanand Gupta, Virendra Jangid, Vaibhav Gupta, Ranjeet Singh Sisodia, Ravindra Arya

Abstract


Face Detection is a type of biometric technique that refers to the detection of a face automatically by computerized systems by taking a look at the face. It is a common function seen in digital cameras, biometrics, and social tagging. Over the past few years, face recognition and detection have drawn increased study attention. In this study, we investigated various methods for face detection and put them into practise using MATLAB software.


Full Text:

PDF

References


Gonzalez Rafael C, Woods Richard E, Eddins Steven L. Digital Image Processing with MATLAB. Pearson Education Society.

Castleman Kenneth R. Digital Image Processing. Pearson Education Society.

Riddhi Patel, Yagnik Shruti B. A Literature Survey on Face Recognition Techniques. Int J Comput Trends Technol (IJCTT). Nov 2013; 5(4): 189–194.

Raunil Singh, Kiran Gupta. Face Detection and Recognition–A Review. SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE). 2015 Apr.

Mathworks. Vision Cascade Object Detector system. https://in.mathworks.com/ help/vision/ref/vision.cascad object detector systemobject.html

Wikipedia. Viola-Jones object detection framework. [Online]. https://en.wikipedia.org/wiki/Viola– Jones_object_detection_framework.

Beymer D, Poggio T. Face recognition from one example view. InProceedings of IEEE International Conference on Computer Vision 1995 Jun 20 (pp. 500-507). IEEE.

Brunelli R, Poggio T. Face Recognition: Features versus Templates. IEEE Trans Pattern Anal Mach Intell. 1993; 15(10): 1042–1052.

MathWorks - Makers of MATLAB and Simulink. Mathworks.com. 2023. Available from: https://in.mathworks.com/

Saber E, Tekalp AM. Face detection and facial feature extraction using color, shape and symmetry-based cost functions. InProceedings of 13th International Conference on Pattern Recognition 1996 Aug 25 (Vol. 3, pp. 654-658). IEEE.

Ballantyne M, Boyer RS, Hines L. Woody Bledsoe: His life and legacy. AI Mag. 1996; 17(1): 7–20.

Hong Duan, Ruohe Yan, Kunhui Lin. Research on Face Recognition Based on PCA. 2008 International Seminar on Future Information Technology and Management Engineering, Leicestershire, UK. 2008; 29–32. 978-0-7695-3480-0/08 2008 IEEE.

Zheng W, Zou C, Zhao L. Real-time face recognition using Gram-Schmidt orthogonalization for LDA. CPR’04; Cambridge, UK. 2004 Aug; 403–406.

Savran A, Alyüz N, Dibeklioğlu H, Çeliktutan O, Gökberk B, Sankur B, Akarun L. Bosphorus database for 3D face analysis. In: Biometrics and Identity Management: First European Workshop, BIOID 2008, Roskilde, Denmark, May 7–9, 2008. Revised Selected Papers. Berlin Heidelberg: Springer; 2008; 47–56.

Bledsoe WW. Semiautomatic facial recognition. Technical report SRI project 6693, Stanford Research Institute, Menlo Park, California. 1968.

Taskiran M, Kahraman N, Erdem CE. Face recognition: Past, present and future (a review). Digit Signal Process. 2020 Nov 1; 106: 102809.

Adjabi I, Ouahabi A, Benzaoui A, Taleb-Ahmed A. Past, present, and future of face recognition: A review. Electronics. 2020 Jul 23; 9(8): 1188.

Kumar A, Kaur A, Kumar M. Face detection techniques: a review. Artif Intell Rev. 2019 Aug 15; 52(2): 927–48.

Jiang F, Lu Y, Chen Y, Cai D, Li G. Image recognition of four rice leaf diseases based on deep learning and support vector machine. Comput Electron Agric. 2020 Dec 1; 179: 105824.


Refbacks

  • There are currently no refbacks.