Adaptive Histogram Equalization to Increase the Percentage of Face Recognition

Biometric system is a self-recognition technology using body parts or human behavior, one of which is the face. The face recognition system is designed to recognize the user by matching the biometric characteristics of the user to the biometric characteristics that have been stored in the database. Many methods can be applied to face recognition systems, such as the Eigenface method that adapts the Principal Component Analysis algorithm. The Eigenface method has a weakness, which is very dependent on the intensity of light. The further the difference in light intensity on the training image and test image, the smaller the percentage of successful face recognition. The adaptive histogram equalization method can be used to overcome the weaknesses of the Eigenface method. The purpose of using the adaptive histogram equalization method is to generalize the intensity of gray values in the Grayscale image and increase the contrast of the image, so that facial features can be further highlighted. The results showed that the use of adaptive histogram equalization can increase the percentage of successful face recognition. Keywords: Biometric, Face Recognition, Eigenface, PCA, Adaptive Histogram Equalization