  This paper describes an efficient approach for human face recognition based
on blood perfusion data from infra-red face images. 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 optical image data. Blood perfusion
data are related to distribution of blood vessels under the face skin. A
distribution of blood vessels are unique for each person and as a 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 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. For classification, a five layer feed-forward backpropagation
neural network has been used. A number of experiments were conducted to
evaluate the performance of the proposed face recognition system with varying
block sizes. Experiments have been performed on the database created at our own
laboratory. The maximum success of 91.47% recognition has been achieved with
block size 8X8.
