Normalization and feature extraction on ear images

Abstract

Ear image analysis is an emerging biometrie application. A method for normalizing ear images and extracting from them a set of measurable features (feature vector) that can be used to identify its owner is proposed. The identification would be made based on the comparison between the feature vector of the input image and all feature vectors of the images in the database we work with. The feature vector is based on the ear contours. One important goal of this paper is to identify the most significant areas in the ear contour for human being identification purpose. Another important contribution of the paper is the combination of active contours techniques and ovoid model ear fitting (used to normalize ear features) and a high accurate invariant approach of internal and external ear contours. Ear geometry is characterized using a set of distances to external and internal contours points. This set of distances, along with six ovoid parameters is considered as the feature vector of the image. To test the method a new ear images database has been created. The proposed method identifies front-parallel views pretty good, even when varying the distance of the individual to the camera or the camera lens.