Feature Selection using Particle Swarm Optimization for Thermal Face Recognition
This paper presents an algorithm for feature selection based on particle swarm optimization (PSO) for thermal face recognition. The total algorithm goes through many steps. In the very first step, thermal human face image is preprocessed and cropping of the facial region from the entire image is done. In the next step, scale invariant feature transform (SIFT) is used to extract the features from the cropped face region. The features obtained by SIFT are invariant to object rotation and scale. But some irrelevant and noisy features could be produced with the actual features. Unwanted features have to be removed. In other words, optimum features have to be selected for better recognition accuracy. The PSO helps to identify the optimum features set using local as well as global searches. Here, this process has been implemented to select a subset of features that effectively represents original feature extracted for better classification convergence. Finally, minimum distance classifier is used to find the class label of each testing images. Minimum distance classifier acts as an objective function for PSO. In this work, all the experiments have been performed on UGC-JU thermal face database. The maximum success rate of 98.61 % recognition has been achieved using SIFT and PSO for frontal face images and 90.28 % for all images.
KeywordsFace recognition Infrared face images Scale invariant feature transform Particle swarm optimization
Authors are thankful to a project supported by DeitY (Letter No.: 12(12)/2012-ESD), MCIT, Government of India and DST-PURSE Programme at Department of Computer Science and Engineering, Jadavpur University, India for providing the necessary infrastructure to conduct experiments relating to this work. Ayan Seal is grateful to Department of Science and Technology (DST), Government of India for providing him Junior Research Fellowship-Professional (JRF-Professional) under DST-INSPIRE Fellowship programme [No: IF110591]. Ayan Seal is also thankful to Universidad Politecnica de Madrid, Spain, for providing him scholarship under Erasmus Mundus Action 2 India4EU II.
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