Evaluating Active Shape Models for Eye-Shape Classification

This paper explores the goal of applying Active Shape Models (ASMs) on the eye images to classify eye shapes and identify whether the images belong from left or right irises. In many applications, particular to data collected from single eye capture devices (such as the PIER mobile iris image acquisition device), it is of importance to be able to sort and correct mislabeled collected data. ASMs have traditionally been applied for classification or identification of a wide variety of objects ranging from faces, assembly line objects to biomedical objects such as bone structures (like the spine etc). In this paper we apply and evaluate ASM models to fit on the eye shape to determine if the image belongs to a left or right eye. The approach we employ is based on building 2 ASM models, one for the left eye and one for right eye. The best fit model is chosen as the result. Our preliminary evaluation using vanilla ASM shows that preprocessing techniques like illumination compensation, shape normalization, and accurate Iris detection are key steps required to improve the classification performance.