Low Resolution Face Recognition System Performance for Basic Feature Space Methods with Various Illumination Normalization Techniques
Manish N. Kapse1, Sunil Kumar2
1Manish N Kapse, Research Scholar, Kalinga University, Naya Raipur, Chhattisgarh, India.
2Sunil Kumar, Professor and Head, Department of E and TC, Kalinga University, Naya Raipur, Chhattisgarh, India.
Manuscript received on October 13, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 4261-4264 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4754119119/2019©BEIESP | DOI: 10.35940/ijitee.A4754.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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 Fisher face. 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 32×32, 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.
Keywords: Feature Space, Feature Extraction, Illumination Normalization, Low Resolution Face Images, LRFR.
Scope of the Article: Image Processing and Pattern Recognition