Adaptive polar transform and fusion for human face image processing and evaluation

Abstract

AbstractHuman face processing and evaluation is a problem due to variations in orientation, size, illumination, expression, and disguise. The goal of this work is threefold. First, we aim to show that the variant of polar transformation can be used to register face images against changes in pose and size. Second, implementation of fusion of thermal and visual face images in the wavelet domain to handle illumination and disguise and third, principal component analysis is applied in order to tackle changes due to expressions up to a particular extent of degrees. Finally, a multilayer perceptron has been used to classify the face image. Several techniques have been implemented here to depict an idea about improvement of results. Methods started from the simplest design, without registration; only combination of PCA and MLP as a method for dimensionality reduction and classification respectively to the range of adaptive polar registration, fusion in wavelet transform domain and final classification using MLP. A consistent increase in recognition performance has been observed. Experiments were conducted on two separate databases and results yielded are very much satisfactory for adaptive polar registration along with fusion of thermal and visual images in the wavelet domain.