Geometrical and eigenvector features for ear recognition

Unconstrained ear biometric means an ear image that has variance in view and pose. This situation is challenging in ear recognition because one ear has various presentation. In this study, two features are considered to handle unconstrained ear image. The features called geometrical feature and eigenvector features. In eigenvector feature, the ear is extracted from six regions then the eigenvector is computed from each of those regions. Each region has capability to represent particular part of the ear image. Another feature is called geometrical feature that reflecting the shape of ear image. The widely used classifier is utilized and it trained with both features. Proposed method outcome is measured to evaluate the recognition rates among single features and fused features. The experiment is carried out on benchmark database collected by University of Science and Technology Beijing (USTB). It shows the proposed method can achieved promising result.

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