The effective noise removal techniques and illumination effect in face recognition using Gabor and Non-Negative Matrix Factorization

Abstract

There is always noise inside the digital images. Noise is an unwanted component of the image. The existence of noise in a face image can degrade the accuracy of a face recognition. Therefore, we need a proper method that can cope noise or restore the quality of the image. The best method to overcome noise in the image is to use smoothing (filter). In this research, we discuss some techniques to overcome noise in face recognition task using Gabor and Non-Negative Matrix Factorization (NMF), as it is stated in the previous research that it still cannot handle images with noise yet. The noises discussed in this research consist of impulse noise (salt-and-pepper), additive noise (Gaussian) and multiplicative noise (speckle). The experiment was conducted by using two face databases; they were ORL and Extended Yale B. The result said that mean filter is the best coping technique for Gabor and NMF face recognition methods. We used K-Nearest Neighbors (KNN) as the classifier and it achieved 90.83% accuracy rate.