Biometric identifier based on hand and hand-written signature contour information

Abstract

The present work presents a biometric identifier system using the combination of two different features: hands shape (finger lengths and width) and hand-written signature contour. Signature database contains 300 different signers with 24 signatures and the hand database has 144 owners with 10 images. The study covers three different classifiers: Hidden Markov Models (HMM), Support Vector Machines (SVM) and a combination of both using the Fisher Kernel. Systems are evaluated separately and in conjunction, giving in each case 100% of identification success rate for the combined classifier. The combination of features gives better results when reducing the training set than the independent systems.