Minutiae Based Thermal Face Recognition using Blood Perfusion Data

TL;DR

This paper presents a novel thermal face recognition method using blood perfusion data, extracting unique minutiae points related to blood vessel distribution, and employing neural networks for classification, achieving up to 91.47% accuracy.

Contribution

It introduces a blood perfusion-based face recognition approach utilizing minutiae points and neural networks, offering improved robustness over optical methods.

Findings

Achieved up to 91.47% recognition accuracy.

Blood perfusion minutiae are unique to individuals.

Optimal block size for feature extraction is 8x8.

Abstract

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.