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Title |
DeepQ based Facial Recognition System in Vanish Lighiting Condition using Convolutional Nerual Netowork |
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Author |
S. Beski Prabaharan, A. Mary Subaja Christo, A. Arun, A. Sasi Kumar, and Nageswara Rao |
| Citation |
Vol. 25 No. 8 pp. 19-26 |
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Abstract |
The main purpose of this method is to verify the hard light face by combining the intensity of the solid light, captions based on caption-based caption, kernel-based feature, and distance-based conversion, Gabor feature, phase component feature and many other features. to combine. This method removes unwanted light effects such as poor lighting - uniform, shadowing, noise, blurring and blurring. Also keep useful information such as Face Features, Eyes, Nose, Ridgets, Rinkles, Shade Area. Pre-chain chain, this method describes our standard lighting method that eliminates the effects of light change. Local ternary (LTP) patterns, typical binary local pattern (LBP) local terminology is very discriminatory and less sensitive to noise in the same areas. Gabor Features, this method is used to filter the image and find selected features. This filter acts as a band pass filter. The final method is Phase congruency features, which evaluates light intensity and identifies the category and size of features. This project was implemented using MATLAB. It is the dominant working language of computer technology. Image Processing Toolbox helps an easy way to work with images in MATLAB like working with any other format. Therefore, MATLAB is best suited for complex image processing applications. |
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Keywords |
Facial Recognition Systems, Vanishing Point, Lighting Conditions, Convolution Neural Network, Matlab |
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