“Feature level fusion of face, palm vein and palm print modalities using Discrete Cosine Transform”

Due to usefulness in recognition and identification biometric systems have become a major part of research. Paper proposes a multimodal biometric system using face modality combined with palm print and palm vein modality. The proposed methodology uses Local Statistical method in which pre-defined block of DCT coefficient were used to calculate standard deviation and store them as feature vector. Matching is done using distance between feature vector of testing and training data set. Results show that the Genuine Acceptance Rate (GAR) of feature level fusion is 100% which is better than, that of uni-modal systems, hence having multimodality is advantageous. For testing and training database of 100 students of College of Engineering Pune.