Person identification system using feature level fusion of multi-biometrics

The use of unimodal biometric system is very low because of physiological defects, modes of user and their environment. Some of those drawbacks are alleviated by providing same identity for multiple evidences. Here a multimodal biometric system is proposed based on LBP, PCA and probabilistic neural network (PNN). In proposed method LBP extracted the Face features from face images and those features are given as input to PCA that generates Face Feature Vector with reduced Dimensions. Finger features are extracted from Fingerprint images using LBP and those features are given as input to PCA that generates Finger Feature Vector with reduced Dimensions. Using LBP, the distinct textual features of face and fingerprint are extracted. Weighted Summation Fusion method is used to combine these unimodal features. A probabilistic neural network is used as Classifier. An average recognition rate of 97.5% achieved with proposed method. Proposed method show that the proposed algorithm requires low computational cost.