Performance of Contrast Adjustment in Face Recognition with Training Image under Various Lighting Conditions
The lighting factor has a very significant effect on facial recognition performance. To reduce the effect of this lighting factor, at the pre-processing stage the researchers used contrast adjustments to the image to improve facial recognition performance. The histogram equalization technique is generally used for contrast adjustment because of its excellent performance to normalize image illumination which is affected by lighting conditions. In this research, empirical experiments were carried out to determine the effect of contrast adjustment using histogram equalization on face recognition in more detail. This research aims to answer the question whether this technique can be used in all image lighting conditions or not. The Robust Regression method is used in this research to recognize faces, which in many cases have very good performance due to lighting factors. Experiments using images in the AR Face Database related to lighting factors. The testing process is carried out by comparing the results of face recognition using the histogram equalization technique in the pre-processing phase and face recognition without pre-processing in each lighting condition. The experimental results show that the use of the histogram equalization technique in pre-processing gives a better face recognition performance effect in low, medium and high lighting conditions. But in very high (extreme) lighting conditions, the use of the histogram equalization technique in pre-processing turns out to have a worse facial recognition performance effect, with an average accuracy of 93.17%, whereas without pre-processing it produces an average accuracy of 94 , 67%.