Abstract
A robust thermal face recognition method has been discussed in this work. A new feature extraction technique named as Histogram of Bunched Intensity Values (HBIVs) is proposed. A heterogeneous classifier ensemble is also presented here. This classifier consists of three different classifiers namely, a five layer feed-forward backpropagation neural network (ANN), Minimum Distance Classifier (MDC), and Linear Regression Classifier (LRC). A comparative study has been made based on other feature extraction techniques for image description. Such image description methods are Harris detector, Hessian matrix, Steer, Shape descriptor, and SIFT. In the classification stage ANN, MDC, and LRC are used separately to identify the class label of probe thermal face images. Another class label is also assigned by majority voting technique based on the three classifiers. The proposed method is validated on UGC-JU thermal face database. The matching using majority voting technique of HBIVs approach showed a recognition rate of 100% for frontal face images which, consists different facial expressions such as happy, angry, etc On the other hand, 96.05% recognition rate has been achieved for all other images like variations in pose, occlusion etc, including frontal face images. The highly accurate results obtained in the matching process clearly demonstrate the ability of the thermal infrared system to extend in application to other thermal-imaging based systems.
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Seal, A., Bhattacharjee, D., Nasipuri, M., Gonzalo-Martin, C., Menasalvas, E. (2014). Histogram of Bunched Intensity Values Based Thermal Face Recognition. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds) Rough Sets and Intelligent Systems Paradigms. Lecture Notes in Computer Science(), vol 8537. Springer, Cham. https://doi.org/10.1007/978-3-319-08729-0_38
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DOI: https://doi.org/10.1007/978-3-319-08729-0_38
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