  In this paper an efficient approach for human face recognition based on the
use of minutiae points in thermal face image is proposed. The thermogram of
human face is captured by thermal infra-red camera. Image processing methods
are used to pre-process the captured thermogram, from which different
physiological features based on blood perfusion data are extracted. Blood
perfusion data are related to distribution of blood vessels under the face
skin. In the present work, three different methods have been used to get the
blood perfusion image, namely bit-plane slicing and medial axis transform,
morphological erosion and medial axis transform, sobel edge operators.
Distribution of blood vessels is unique for each person and a set of extracted
minutiae points from a blood perfusion data of a human face should be unique
for that face. Two different methods are discussed for extracting minutiae
points from blood perfusion data. For extraction of features entire face image
is partitioned into equal size blocks and the total number of minutiae points
from each block is computed to construct final feature 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 methodologies with varying
block size on the database created at our own laboratory. It has been found
that the first method supercedes the other two producing an accuracy of 97.62%
with block size 16X16 for bit-plane 4.
