Multimodal biometrics by integrating fingerprint and palmprint for security

Abstract

Multimodal Biometrics there is a combination of different biometric modalities. In most biometric systems from real world applications uses a single source of biometric modality for authentication that is only a single fingerprint, palm print, face, voice or iris which is known as unimodal biometrics. Some of the drawbacks from unimodal biometrics are eliminated by combining information from multiple biometrics systems for unique personal identity. This paper gives a multimodal biometrics system that combines features of fingerprint and palm print to overcome several limitations of unimodal biometrics. Preprocessing is a heart of image processing in which feature enhancement is prior step, in this system feature enhancement of input image that is fingerprint and palm print are performed by applying a series of preprocessing techniques. Modified Gabor filter is used to independently extract a fingerprint and palmprint feature which provides more accuracy as compared to traditional Gabor filter In addition to this Short Time Fourier transformation is applied for better quality of resultant images. In the later step the resultant images are combined in feature level fusion method and finally using Euclidean distance method the features are classified to match the resultant image with database template. We conclude that proposed methodology has better performance as compared to unimodal approaches using individually only a fingerprint or a palm print. The multiple biometrics helps to reduce the system error rate.