|dc.description.abstract||Nowadays, human identification is more and more important in security. The most important identification method is the use of the biometric feature. Either the commodities which recognize the authorized user with fingerprint or customs officers use the iris recognition system to identify passengers, they elaborate the convenient and the reliable of biometric identification. In the past, a lot of researches on fingerprint, voiceprint, palmprint, human face and iris. They use kinds of algorithms to find out stable feature which differs from person to person for identification. In our approach, we devise a method combine visible images with thermal images for identification.
These two kinds of different images have pros and cons. They capture electromagnetic radiation in different ranges and show they include different information. Features extracted from visual images by classical method, Fisherface method. From thermal images, we get the temperature distribution, by physiology phenomenon, based on temperature gradient and morphology. We use the local square windows to count pixels of a net to make feature vectors indicating images, which is called counter filter. Finally, we use two feature vectors and turn them into longer vectors, and then classify them with KNN classifier. Experimental results demonstrate that the performance of the system with multi-model is better than one with a single model.||en_US|