Thermal heat distribution features for hand identification

Thermal imaging is a non-invasive and portable technique with growing use in medical and authentication applications. This research utilizes thermal images for hand identification. Existing hand identification methods mainly extract geometric features, such as the palm's and fingers’ absolute sizes and ratios. In this work, subject identification based on spatial statistical thermal distribution features is examined. These features do not depend on the geometric shape of the hand, but rather capture only physiological properties and convert them to statistical features. Thus, our goal is to evaluate the ability to identify a hand by characterizing the thermal heat distribution pattern, without relaying on geometric proprieties. A novel image processing algorithm, which identifies and locates the hand posture from the image and extracts several features from 15 locations in the hand, was developed. Then, dimensionality reduction methods are applied to form a compact model of the data. The best model was selected based on the clustering quality. Classification of a single subject out of many resulted with accuracy of 94%. Classification of many subjects simultaneously resulted with accuracy of 91%. Identification of new image data is carried out in this low-dimensional space and results with an accuracy of 94%. In addition, the low-dimensional representation was used to explore time and environmental factors, which were found to have a low impact on the coded data, thus promoted the suitability of the proposed method.