Open AccessFace recognition system based on principal components analysis and distance measures
Author(s) -
Toshanlal Meenpal,
Aarti Goyal,
Ankita Meenpal
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.21.11826
Subject(s) - artificial intelligence , facial recognition system , pattern recognition (psychology) , computer science , principal component analysis , mahalanobis distance , biometrics , feature extraction , euclidean distance , face (sociological concept) , eigenface , distance measures , computer vision , three dimensional face recognition , dimensionality reduction , face detection , social science , sociology
Face recognition plays a vital role and has a huge scope in the field of biometrics, image processing, artificial intelligence, pattern recognition and computer vision. This paper presents an approach to perform face recognition using Principal Components Analysis (PCA) as feature extraction technique and different distance measures as matching techniques. The proposed method is developed after the deep study of a number of face recognition methods and their outcomes. In the proposed method, Principal Components Analysis is used for facial features extraction and data representation. It generates eigenvalues of the facial images, hence, reduces the dimensionality. The recognition is produced using three different matching techniques (Euclidean, Manhattan and Mahalanobis) and the results are` presented. Yale and Aberdeen Face Databases are used to test and analyze the results of the proposed method.