A Comparative Study of Human Thermal Face Recognition Based on Haar Wavelet Transform and Local Binary Pattern

摘要

Thermal infrared (IR) images focus on changes of temperature distribution on facial muscles and blood vessels. These temperaturechanges can be regarded as texture features of images. A comparative study of face two recognition methods working in thermalspectrum is carried out in this paper. In the first approach, the training images and the test images are processed with Haar wavelettransform and the LL band and the average of LH/HL/HH bands subimages are created for each face image. Then a total confidencematrix is formed for each face image by taking a weighted sum of the corresponding pixel values of the LL band and average band. For LBP feature extraction, each of the face images in training and test datasets is divided into 161 numbers of subimages, each ofsize 8 × 8 pixels. For each such subimages, LBP features are extracted which are concatenated in manner. PCA is performedseparately on the individual feature set for dimensionality reduction. Finally, two different classifiers namely multilayer feedforward neural network and minimum distance classifier are used to classify face images. The experiments have been performed onthe database created at our own laboratory and Terravic Facial IR Database.