Influence of quality of images recorded in far infrared on pattern recognition based on neural networks and Eigenfaces algorithm

Lukasz Jelen; Joanna Kobel; Halina Podbielska M.D.

doi:10.1117/12.545143

21 November 2003 Influence of quality of images recorded in far infrared on pattern recognition based on neural networks and Eigenfaces algorithm

Lukasz Jelen, Joanna Kobel, Halina Podbielska M.D.

Author Affiliations +

Proceedings Volume 5259, 13th Polish-Czech-Slovak Conference on Wave and Quantum Aspects of Contemporary Optics; (2003) https://doi.org/10.1117/12.545143
Event: 13th Polish-Czech-Slovak Conference on Wave and Quantum Aspects of contemporary Optics, 2002, Krzyzowa, Poland

Abstract

This paper discusses the possibility of exploiting of the tennovision registration and artificial neural networks for facial recognition systems. A biometric system that is able to identify people from thermograms is presented. To identify a person we used the Eigenfaces algorithm. For the face detection in the picture the backpropagation neural network was designed. For this purpose thermograms of 10 people in various external conditions were studies. The Eigenfaces algorithm calculated an average face and then the set of characteristic features for each studied person was produced. The neural network has to detect the face in the image before it actually can be identified. We used five hidden layers for that purpose. It was shown that the errors in recognition depend on the feature extraction, for low quality pictures the error was so high as 30%. However, for pictures with a good feature extraction the results of proper identification higher then 90%, were obtained.

Citation Download Citation

Lukasz Jelen, Joanna Kobel, and Halina Podbielska M.D. "Influence of quality of images recorded in far infrared on pattern recognition based on neural networks and Eigenfaces algorithm", Proc. SPIE 5259, 13th Polish-Czech-Slovak Conference on Wave and Quantum Aspects of Contemporary Optics, (21 November 2003); https://doi.org/10.1117/12.545143

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