Abstract
In face recognition system, the accuracy of recognition is greatly affected by varying degree of illumination on both the probe and testing faces. Particularly, the changes in direction and intensity of illumination are two major contributors to varying illumination. In overcoming these challenges, different approaches had been proposed. However, the study presented in this paper proposes a novel approach that uses deep learning, in a MATLAB environment, for classification of face images under varying illumination conditions. One thousand one hundred (1100) face images employed were obtained from Yale B extended database. The obtained face images were divided into ten (10) folders. Each folder was further divided into seven (7) subsets based on different azimuthal angle of illumination used. The images obtained were filtered using a combination of linear filters and anisotropic diffusion filter. The filtered images were then segmented into light and dark zones with respect to the azimuthal and elevation angles of illumination. Eighty percent (80%) of the images in each subset which forms the training set, were used to train the deep learning network while the remaining twenty percent (20%), which forms the testing set, were used to test the accuracy of classification of the deep learning network generated. With three successive iterations, the performance evaluation results showed that the classification accuracy varies from 81.82% to 100.00%.
Highlights
Face recognition technology, which is one of the most successful applications of computer vision and image processing, has over the last two decades gained recognition within the computer vision community
We evaluate the applicability of deep convolutional neural network (Deep Learning) for the classification problem described in this work
The MATLAB codes written was run on a single intel ® Pentium® CPU having a speed of 2.16 GHz
Summary
Face recognition technology, which is one of the most successful applications of computer vision and image processing, has over the last two decades gained recognition within the computer vision community This is accountable for its wide range of applications in several fields. Development of face recognition systems has reached a certain level of maturity, their successes are limited by the conditions imposed by many real applications Some of these conditions can be due to variation in pose, illumination, expression and age [1]-[2]. According to [6] the gradient face method is an illumination insensitive measure, which is quite robust to different illumination intensities including uncontrolled natural lighting This method involves the extraction of gradient faces from face image gradient domain so as to discover the underlying features that are peculiar to a particular face image. Two other techniques that have been developed by researchers to tackle the problem of illumination variation are the histogram [7] and Gabor filters [8]
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