Ear Features based Human Identification System

Biometric features such as fingerprint, Iris were considered in the past for human identification. Ear biometrics can be considered as a new class of human identification system. The performance of ear biometric system mainly depends on proper segmentation of ear from the image and the feature extracted from the segmented ear image. It is observed from the literature, that most of the ear identification methods are not automatic. It needs user intervention in segmenting the ear from the image in order to process further. In this paper, an approach for fully automated ear segmentation and “Adaptive blocks” based feature extraction technique was proposed. Along with the extracted features, the Histogram of Oriented Gradients (HOG) features will be extracted from the ear image. The human will be identified by designing a suitable Artificial Neural Network (ANN) with the extracted ear features as the inputs and the image index as the targets. The performance of the network will be studied with different test ear images.