In biometrics research face detection and recognition is a very popular topic and it has distinct advantages because of its non-contact process. This type of technology extensively draws attention due to its huge application and market value. like video surveillance system for detecting suspicious object. Face based recognition system is more popular over other biometrics because of its uniqueness. Face recognition is very difficult task because human face is a dynamic object and has variability in its appearance. So, here accuracy and speed of recognition is Min issue. The purpose of the paper is correctly recognized a person from an image face or a video. To correctly identify a person we have used three techniques: Linear discriminant analysis (LDA), Logistic Regression (LR) and support vector Machine (SVM) techniques with Principle Components Analysis (PCA) which extract the features and reduce dimensionality. The LDA and LR technique produce more accurate result compare to other methods. This paper achieved 93% successful recognition rate for recognizing different face database.

Face detection, Face Recognition, PCA, SVM, LDA, and LR

[1] R. Brunelli and T. Poggio, “Face recognition: Feature versus templates”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 15 , Issue.10 , pp. 1042 – 1052
[2] K.J. Wang, SH.L. Duan & W.X. Feng (2008), “A Survey of Face Recognition using Single Training Sample”, Pattern Recognition and Artificial Intelligence, China, Vol. 21, Pp. 635–642.
[3] Han Bing, “Research of Face Detection Based on AdaBoost and ASM”, The Open Cybernetics & Systemics Journal, 2014, 8, pp.183-190.
[4] Mrs. Madhuram. M, B. Prithvi Kumar, “Face Detection and Recognition Using OpenCV”, International Research Journal of Engineering and Technology (IRJET), | ISO 9001:2008, e-ISSN: 2395-0056, Volume: 05, Issue: 10.
[5] G. Gibert, D. D`Alessandro and F. Lance, "Face detection method based on photoplethysmography", IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013, p. 449-453
[6] R. Niese, “Facial expression recognition based on geometric and optical flow features in colour image sequences”, Vol. 6, Iss. 2, pp. 79–89, doi: 10.1049/iet-cvi.2011.0064.
[7] X. Yi, W. Ying and P. Jun, "An improved adaboost face detection algorithm based on the weighting parameters of weak classifier"; In: Proceedings of the 12th IEEE International Conference on Cognitive Informatics and Cognitive Computing, 2013, pp. 347-350.
[8] Mrs. Madhuram. M, “Face Detection and Recognition Using OpenCV”, International Research Journal of Engineering and Technology (IRJET), e-ISSN: 2395-0056, p-ISSN: 2395-0072, Volume: 05, Issue: 10.
[9] Face Recognition using Principle Component Analysis Kyungnam Kim Department of Computer Science University of Maryland, College Park MD 20742, USA
[10] Caifeng Shan,” Smile Detection by Boosting Pixel Differences”, IEEE Transactions On Image Processing, VOL.21, NO. 1, JANUARY 2012, Page(s): 431 – 436, DOI: 10.1109/TIP.2011.2161587.
[11] K. Rajakumari, “An Evaluation of Face Recognition By using Principal Component Analysis with Support Vector Machine”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Volume-7 Issue-6S3 April, 2019
[12] Manav Bansal,” Facial Detection & Recognition Using Open CV library”, IJCST Vol. 7, Issue 1, Jan -March 2016 ISSN: 0976-8491 (Online) | ISSN :2229-4333
[13] Shilpa Sharma,” Face Recognition using PCA and SVM with Surf Technique”, International Journal of Computer Applications (0975 – 8887) Volume 129 – No.4, November2015.
[14] Shailaja A. Patil, “Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) based Face Recognition”, International Journal of Computer Applications (0975 - 8887).