Fusion of fingerprint, palmprint and hand geometry for an Efficient Multimodal person authentication system

Biometric framework is effectively emerging in different commercial ventures for the past couple of years, and it is continuing to offer higher security features for access control system. Numerous sorts of unimodal biometric frameworks have been produced. But, these frameworks are only able to provide low to medium range of security features. As a result, for higher security features, the composite of two or more unimodal biometrics (different modalities) is required. In this paper, we propose a multimodal biometric framework for person authentication by incorporating three distinct modalities, such as, Fingerprint, palmprint and hand geometry. Unique fingerprint recognition is carried out utilizing Histogram of oriented gradients (HOG), palmprint recognition is performed utilizing combination of principal component analysis (PCA) and linear discriminant analysis (LDA) where as hand geometry recognition is accomplished utilizing Harris corner detection algorithm. The three modalities are consolidated and the fusion is applied at the feature level for both verification and identification. Integrating several biometric traits increases recognition performance and decreases fraudulent access. The identity established by this system is more reliable and robust than the identity established by individual biometric systems. The classification is performed using a matching technique such as support vector machine (SVM). The system was tested on the database of 50 persons. The experimental results revealed that the designed system attains an excellent recognition rate and offer more security than unimodal biometric-based system. The recognition performance of this recognition system is computed by means of False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Rate (RR).