Minutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm

TL;DR

This paper proposes a thermal infrared face recognition system that uses blood perfusion data and neural networks, demonstrating high performance on laboratory-created images.

Contribution

It introduces a novel thermal face recognition method combining blood perfusion features with neural network classification.

Findings

High recognition accuracy achieved on laboratory thermal images

Effective extraction of blood perfusion features from thermal face images

System demonstrates robustness in controlled experimental conditions

Abstract

In this paper, a thermal infra red face recognition system for human identification and verification using blood perfusion data and back propagation feed forward neural network is proposed. The system consists of three steps. At the very first step face region is cropped from the colour 24-bit input images. Secondly face features are extracted from the croped region, which will be taken as the input of the back propagation feed forward neural network in the third step and classification and recognition is carried out. The proposed approaches are tested on a number of human thermal infra red face images created at our own laboratory. Experimental results reveal the higher degree performance