Open AccessLow Resolution Face Recognition System Performance for Basic Feature Space Methods with Various Illumination Normalization Techniques
Author(s) -
Manish N. Kapse,
Sunil Kumar
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a4754.119119
Subject(s) - artificial intelligence , normalization (sociology) , facial recognition system , computer vision , computer science , biometrics , low resolution , feature extraction , face (sociological concept) , pattern recognition (psychology) , feature (linguistics) , three dimensional face recognition , high resolution , face detection , geography , remote sensing , social science , linguistics , philosophy , sociology , anthropology
The Face recognition research identified with the field of automated surveillance systems in real life application has pulled in more consideration today with extensive use of vision cameras in biometrics and surveillance applications, globally. The small size images or poor quality images are generally Low Resolution images. Authenticating faces, with variation in pose, illumination, disguise and more, from such Low-resolution images, is the main purpose of Low Resolution Face Recognition (LR FR) system. We have assessed the LR FR system for various basic feature space techniques like PCA, LDA and Fisherface. Different illumination normalization techniques are applied on the cropped Yale face database prior to feature extraction and identification. In our work the low-resolution images, of size 32x32, are the down-sampled versions high-resolution facial images from cropped Yale face database. Our experiment demonstrates the encouraging performance, with recognition accuracy as 97.43%, on Low Resolution, Low quality face images.