Ear recognition system using adaptive approach Runge–Kutta (AARK) threshold segmentation with ANFIS classification

In the field of biometrics, ear recognition is a niche idea of recognition for human authentication, which has several merits compared to the other biometric recognitions like face and finger print. The contour of an ear is distinctive for each person, which is the main reason for choosing this recognition technique. Only in very few studies, the ear recognition algorithms were presented. There still remains a large space for research in the field of ear biometrics. All recent papers have implemented ear recognition algorithms using 2-D ear images. The ear recognition algorithms should be efficient in order to provide accurate results, owing to issues like multiple poses and directional related. This paper proposes a novel method for segmentation based on adaptive approach Runge–Kutta (AARK) to recognize ear images. AARK threshold segmentation technique is used for finding the threshold value to determine the region to be segmented. The utilization of AARK’s numerical methods in computing the threshold value for ear recognition process improves the result accuracy. Firstly, preprocessing has been carried out for the dataset. The following steps are carried out sequentially: ring projection, information normalization, morphological operation, AARK segmentation, feature extraction of DWT and finally ANFIS classifier are used. Among the various steps mentioned, ring projection converts the two dimensions into single dimensions. The self-adaptive discrete wavelet transform is used to extract features from the segmented region. Then the ANFIS classifier is used to recognize the ear region from the image by taking the features form the test image and the training images. The proposed method obtained 72% improvement in PSNR and accuracy is improved to 63.3%. Moreover, the speed and space occupation of the self-adaptive DWT technique and the conventional DWT technique are measured by implementing the methods in FPGA Spartan 6 device. Comparing with the implementation of conventional DWT, the area is reduced to 361 from 7021 while implementing the proposed self-adaptive DWT method.