Face Detection and Recognition using Machine Learning Techniques

Author:

S. Vijayalakshmi ,J. Uma Maheswari ,K. Jananiyie

Abstract

Face recognition of persons has received so much attention in the recent years due to its many applications in different fields such as security applications, video surveillance, biometric systems, identifying the criminals etc. This paper develops a system that can recognize the human face in the input image after it has been detected. The system is trained with set of faces and non faces, and when the input picture is given, the face is detected using Viola Jones Algorithm. In face recognition, the features are extracted from the training dataset using Principal Component Analysis (PCA) and then the system is trained to recognize the face using Support Vector Machine (SVM) classification. When the input image is given for face recognition, features are extracted from the input picture using PCA and multiclass classification is done by SVM.

Publisher

Inventive Research Organization

Subject

General Agricultural and Biological Sciences

Reference10 articles.

1. [1] Liton Chandra Paul, Abdulla Al Suman. “Face Recognition using Principal Component Analysis Method,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1, no.09 (2012), 135-139.

2. [2] Venkata Narayana, K., Manoj, V.V.R., Swathi, K. “Enhanced Face Recognition based on PCA and SVM.” International Journal of Computer Applications, 7, no.02 (2015): 40-42.

3. [3] Hongjun Jia & Aleix, M, Martinez. “Support Vector Machines in Face Recognition with Occlusions, Available”: http://www2.ece.ohio-state.edu/~aleix/CVPR09.pdf. [Accessed: 07- Oct- 2022].

4. [4] Jonathon Phillips P. “Support Vector Machines Applied to Face Recognition,” National Institute of Standards and Technology, https://nvlpubs.nist.gov/nistpubs/Legacy/IR/nistir6241.pdf. [Accessed: 17- Aug- 2022].

5. [5] Jindal Vikas Kumar, N.(2013). Enhanced Face Recognition Algorithm using PCA with Artificial Neural Networks. International Journal of Advanced Research in Computer Science and Software Engineering, 3. no.06(2013)

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