Can thermal and visible image fusion improves ear recognition?

This paper intends to study whether thermal and visible image fusion improves ear recognition given illumination variations. Five fusion methods, namely, simple average, mean average, average discrete wavelet transform (DWT), optimized DWT and weighted DWT were tested. The experiments were done using DIAST Variability Illuminated Thermal and Visible Ear Image Datasets. There were two experiments conducted. The first experiment is done using images with three different illumination conditions (i.e. dark, average and bright illuminations) while the second experiment only considers average and brightly illuminated image. The evaluation was done based on ear recognition accuracy where features were extracted based on the histogram of oriented gradients (HOG) while support vector machine (SVM) was used as the classifier. In Experiment 1, thermal images performed best with 96.36% recognition rate while visible images was the lowest with 62.73% accuracy. In Experiment 2, all three DWT fusions scored 95.45% accuracy, surpassing thermal image (94.55%) while visible image still is the lowest with 90.00% recognition rate.