Minutiae from bit-plane sliced thermal images for human face recognition

Abstract

In this paper an efficient approach for human face recognition based on the use of minutiae points is proposed. The thermogram of human face is captured by thermal infra-red camera. Image processing technologies are used to pre-process the captured thermogram. Then different physiological features are extracted using bit-plane slicing from the captured thermogram. These extracted features are called blood perfusion data. Blood perfusion data are characterized by the regional blood flow in human tissue and therefore do not depend entirely on surrounding temperature. These data bear a great potential for deriving discriminating facial thermogram for better classification and recognition of face images in comparison to static image data. Blood perfusion data are related to distribution of blood vessels under the face skin. Distribution of blood vessels is unique for each person and as set of extracted minutiae points from a blood perfusion data of a human face should be unique for that face. There may be several such minutiae point sets for a single face but all of these correspond to that particular face only. Entire face image is partitioned into equal consequence blocks and the total number of minutiae points from each block is computed to construct final vector. Therefore, the size of the feature vectors is found to be same as total number of blocks considered. A five layer feed-forward back propagation neural network is used as the classification tool. A number of experiments were conducted to evaluate the performance of the proposed face recognition system with varying block size. Experiments have been performed on the database created at our own laboratory. The maximum success of 95.24% recognition has been achieved with block size 8x8 and 32x32 with bit-plane 4 and accuracy rate of 97.62% has been achieved with block size 16x16 for bit-plane 4. © 2012 Springer India Pvt. Ltd.