An integrated approach for multimodal biometric recognition system using Pearson type-II (beta) distribution

Biometric recognition plays an important role in personnel identity authentication. Usually, biometric recognition protocols which involve single source of information are called unimodal systems. Such systems suffer from the problems like noisy sensor data, performance, collectability and non-universality. To have an accurate recognition, it is needed to develop a system with multimodal biometrics. Hence, in this paper, a new approach is proposed with the combination of multiple biometric traits such as face, fingerprint and palm vein. Region of interest (ROI) is used to consider the valuable information from the images. The 2D discrete cosine transform is used for extracting the feature vector from face, fingerprint and palm vein and fusion at feature extraction level. Here, the feature vector is modelled with Pearson type-II distribution and the model parameters are estimated using the EM algorithm. The initialisation of model parameters is done through moment method of estimators and K-means algorithm. The performance of the proposed algorithm is carried by experimentation with CASIA biometric database. Through experimentation, the proposed model performs more effectively than the algorithm with Gaussian mixture model.