Illumination invariant face recognition using contourlet transform and convolutional neural network

Abstract

The face is a dominant biometric for recognizing a person. However, face recognition becomes challenging when there are severe changes in lighting conditions, i.e., illumination variations, which have been shown to have a more severe effect on recognition performance than the inherent differences between individuals. Most of the existing methods for tackling the problem of illumination variation assume that illumination lies in the large-scale component of a facial image; as such, the large-scale component is discarded, and features are extracted from small-scale components. Recently, it has been shown that large-scale component is also important; in addition, small-scale component contains detrimental noise features. Keeping this in view, we introduce a method for illumination invariant face recognition that exploits large-scale and small-scale components by discarding the illumination artifacts and detrimental noise using ContourletDS. After discarding the unwanted components, local and global features are extracted using a convolutional neural network (CNN) model; we examined three widely employed CNN models: VGG-16, GoogLeNet, and ResNet152. To reduce the dimensions of local and global features and fuse them, we employ linear discriminant analysis (LDA). Finally, ridge regression is used for recognition. The method was evaluated on three benchmark datasets; it achieved accuracies of 99.7%, 100%, and 79.76% on Extended Yale B, AR, and M-PIE, respectively. The comparison reveals that it outperforms the state-of-the-art methods.

Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain

“…In addition, our new algorithm is very robust to Gaussian white noise due to the introduction of bivariate wavelet shrinkage. Our method is easy to implement, and it yields higher recognition rates than several existing methods for both Extended Yale Face Database B [5] and CMU-PIE illumination face database [24] no matter there exist noise in the face images or not.…”

“…Furthermore, the training of this network does not need a very big dataset. Hussain et al [24] developed a new algorithm for illumination invariant face recognition, which takes advantage of large-scale and small-scale components by discarding the illumination artifacts and detrimental noise using Contourlets. After discarding the unwanted components, local and global features are extracted using a convolutional neural network (CNN) model.…”