Minutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm
This work addresses human identification and verification using thermal imaging, but it appears incremental as it applies standard methods to a new type of data.
The paper tackled thermal infrared face recognition by extracting minutiae features from blood perfusion data and using a backpropagation neural network for classification, achieving a higher degree of performance in experiments on a custom dataset.
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