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In this work we investigate a novel approach to handle the challenges of facerecognition, which includes rotation, scale, occlusion, illumination etc. Here,we have used thermal face images as those are capable to minimize the affect ofillumination changes and occlusion due to moustache, beards, adornments etc.The proposed approach registers the training and testing thermal face images inpolar coordinate, which is capable to handle complicacies introduced by scalingand rotation. Line features are extracted from thermal polar images and featurevectors are constructed using these line. Feature vectors thus obtained passesthrough principal component analysis (PCA) for the dimensionality reduction offeature vectors. Finally, the images projected into eigenspace are classifiedusing a multi-layer perceptron. In the experiments we have used Object Trackingand Classification Beyond Visible Spectrum (OTCBVS) database. Experimentalresults show that the proposed approach significantly improves the verificationand identification performance and the success rate is 99.25%.

Human Face Recognition using Line Features