MULTIMODAL BIOMETRICS BY FACE AND HAND IMAGES TAKEN BY A CELL PHONE CAMERA

Abstract

This paper presents a multimodal approach for a biometrics verification system. It is based on face and hand images captured by a cell phone. The algorithm includes all parts that are required for face and hand verification, such as feature extraction, classification and authentication. To find local facial features, such as eyes, mouth and nose, we apply a point distribution model and active shape models. We use the same system to find distinctive points in hand geometry. The face feature vector is constructed by applying a Gabor filter to the image and extracting the key points found by an active shape model. The palm feature vector contains characteristics of the hand geometry features. A support vector machine (SVM) is applied to verify the identity of the user. One SVM machine is built for each person in the database to distinguish that person from others. To test the algorithm we built our own database containing face and hand images taken by a cell phone camera. The database contains 480 frontal face images and 120 hand images of 30 persons (16 face images and 4 hand images per person).