Face Recognition using Face-Autocropping and Facial Feature Points Extraction

Abstract

Different facial feature extraction schemes are available in face recognition literature where the face is cropped and feature points are extracted using mathematical formulae along with probabilistic distance measure between major feature points. In most of the cases the face cropping is done manually and the formulae for extracting a feature point are highly complicated. In this article a facial feature extraction method is proposed where color face images are auto-cropped and control points are extracted, both using the same segmentation mechanism. The segmentation method is first used to auto crop the image and then again applied on this auto cropped image for the detection of major connected components. The feature points are detected simply using the geometrical measurement of location and size of the component without any a priory knowledge of the probabilistic distance between the feature points or using any feature point extraction formula. A T-shaped face image is formed comprising of major feature points. Recognition rate on the unprocessed face images, using PCA, is recorded. PCA is applied on the T-shaped faces and the improvement in rate of recognition is concluded to be statistically significant.