An Adaptive Entropy Based Scale Invariant Face Recognition Face Altered by Plastic Surgery

Abstract

Face recognition is one of the challenging problems which suffer from practical issues like the pose, expression, illumination changes, and aging. Plastic surgery is one among the issues that pose great difficulty in recognizing the faces. The literature has been reported with traditional features and classifiers for recognizing the faces after plastic surgery. This paper presents an adaptive feature descriptor and advanced classifier for plastic surgery face recognition. According to the proposed feature descriptor, firstly an adaptive Gaussian transfer function is determined to perform Adaptive Gaussian Filtering (AGF) for images. Secondly, Adaptive Entropy-based SIFT (AEV-SIFT) features are extracted from the filtered images. Unlike traditional SIFT, the proposed AEV-SIFT extracts the key points based on the entropy of the volume information of the pixel intensities. This provides the least effect on uncertain variations in the face because the entropy is the higher order statistical feature. Further, the classification is performed with variations. In the first variation, support vector machine (SVM) is used as a classifier, whereas the second variation exploits the Deep Learning Network (DLN) for recognizing the faces based on the AEV-SIFT features. The proposed method classifies the plastic surgery face images with the accuracy of 80.15%, sensitivity of 19.75% and specificity of 95%, which are obviously better than the traditional features such as SIFT, V-SIFT, and Principal Component Analysis (PCA).