An automated feature-localisation algorithm for a feature-specific modular approach for face recognition

Abstract

Novel techniques for accurate location of the eyes and nose of a person in a complex-lighting environment are presented in this paper. An adaptive progressive thresholding technique is applied to spot the darkest regions representing the eyes in a face. The nose region is located by performing cumulative histogram-based thresholding of the gradient image formed below the eye region. A feature-specific modular Principal Component Analysis (PCA) approach on face images is performed with the identified features for face recognition. Principal components are extracted from non-overlapping modules of the image and are concatenated to make a single signature vector to represent the face in a particular viewing angle. Additional principal components are extracted from the key facial features and are added as an extension to the signature vector. The feature-specific modular PCA approach is capable of recognising faces in varying illumination conditions and facial expressions, as the modular components represent the local information of the facial regions.