“Feature level fusion of palm print and fingerprint modalities using Discrete Cosine Transform”

Biometric systems have become a major part of research due its application of identification. Paper proposes a multimodal biometric system using palm prints modality combined with fingerprint modality. The proposed methodology uses standard deviation of pre-defined block of DCT coefficient as feature vector. Recognition process is being done by performing distance measurement between feature vector of testing and training data set. Results show that the False Acceptance Rate (FAR) of feature level fusion is less than that of uni-modal systems, hence having multimodality is advantageous. Testing and training is done on database of 150 students of College of Engineering Pune.