Multimodal Biometrics
ABSTRACT:
This paper presents a novel multi-level wavelet based fusion algorithm that combines information from fingerprint, face, iris, and signature images of an individual. The effectiveness of the fusion algorithm is experimentally validated by computing the matching scores and the equal error rates before fusion, after reconstruction of biometric images, and when the composite fused image is subjected to both frequency and geometric attacks. The complexity of the fusion and the reconstruction algorithms is O(n log n) and is suitable for many real-time applications. The final decision is made by fusion at “matching score level architecture” in which feature vectors are created independently for query images and are then compared to the enrollment templates which are stored during database preparation for each biometric trait.
We also propose a multi-modal biometric algorithm that further reduces the equal error rate compared to individual biometric images. The proposed approach reduces the memory size, increases the recognition
accuracy using multi-modal biometric features, and withstands common attacks such as smoothing, cropping, and filtering due to tampering.
INTRODUCTION:
“Biometrics” means “life measurement”, but the term is usually associated with the use of unique physiological characteristics to identify an individual. One of the applications which most people associate with biometrics is security. It is an automated method of recognizing a person based on the features face, fingerprints, hand geometry, handwriting, iris, retinal, vein, voice etc. Biometric technologies are becoming the foundation of an extensive array of highly secure identification and personal verification solutions. As the level of security breaches and transaction fraud increases, the need for highly secureidentification and personal verification technologies is becoming apparent.
In particular, biometric authentication systems generally suffer from
This paper presents a novel multi-level wavelet based fusion algorithm that combines information from fingerprint, face, iris, and signature images of an individual. The effectiveness of the fusion algorithm is experimentally validated by computing the matching scores and the equal error rates before fusion, after reconstruction of biometric images, and when the composite fused image is subjected to both frequency and geometric attacks. The complexity of the fusion and the reconstruction algorithms is O(n log n) and is suitable for many real-time applications. The final decision is made by fusion at “matching score level architecture” in which feature vectors are created independently for query images and are then compared to the enrollment templates which are stored during database preparation for each biometric trait.
We also propose a multi-modal biometric algorithm that further reduces the equal error rate compared to individual biometric images. The proposed approach reduces the memory size, increases the recognition
accuracy using multi-modal biometric features, and withstands common attacks such as smoothing, cropping, and filtering due to tampering.
INTRODUCTION:
“Biometrics” means “life measurement”, but the term is usually associated with the use of unique physiological characteristics to identify an individual. One of the applications which most people associate with biometrics is security. It is an automated method of recognizing a person based on the features face, fingerprints, hand geometry, handwriting, iris, retinal, vein, voice etc. Biometric technologies are becoming the foundation of an extensive array of highly secure identification and personal verification solutions. As the level of security breaches and transaction fraud increases, the need for highly secureidentification and personal verification technologies is becoming apparent.
In particular, biometric authentication systems generally suffer from
References: [2] A. Ross & A. K. Jain, Information Fusion in Biometrics, Pattern Recognition Letters, 24 , pp. 2115-2125, 2003.