  Thermal infrared (IR) images represent the heat patterns emitted from hot
object and they do not consider the energies reflected from an object. Objects
living or non-living emit different amounts of IR energy according to their
body temperature and characteristics. Humans are homoeothermic and hence
capable of maintaining constant temperature under different surrounding
temperature. Face recognition from thermal (IR) images should focus on changes
of temperature on facial blood vessels. These temperature changes can be
regarded as texture features of images and wavelet transform is a very good
tool to analyze multi-scale and multi-directional texture. Wavelet transform is
also used for image dimensionality reduction, by removing redundancies and
preserving original features of the image. The sizes of the facial images are
normally large. So, the wavelet transform is used before image similarity is
measured. Therefore this paper describes an efficient approach of human face
recognition based on wavelet transform from thermal IR images. The system
consists of three steps. At the very first step, human thermal IR face image is
preprocessed and the face region is only cropped from the entire image.
Secondly, Haar wavelet is used to extract low frequency band from the cropped
face region. Lastly, the image classification between the training images and
the test images is done, which is based on low-frequency components. The
proposed approach is tested on a number of human thermal infrared face images
created at our own laboratory and Terravic Facial IR Database. Experimental
results indicated that the thermal infra red face images can be recognized by
the proposed system effectively. The maximum success of 95% recognition has
been achieved.
