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.
“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 enrollment problems due to non-universal biometric traits, susceptibility to biometric spoofing or insufficient accuracy caused by noisy data acquisition in certain environments. The algorithm includes all parts that are required for face and hand verification, such as feature extraction, classification and authentication. To find local facial features, such as eyes, mouth and nose, we apply a point distribution model and active shape models. We use the same system to find distinctive points in hand geometry.
NECESSITY FOR MULTIMODAL BIOMETRICS:
One way to overcome the problems in unimodal is the use of multi-biometrics. Driven by lower hardware costs, a multi biometric system uses multiple sensors for data acquisition.. Further, if the biometric trait being sensed or measured is noisy (a fingerprint with a scar or a voice altered by a cold, for example), the resultant matching score computed by the matching module may not be reliable. This problem can be solved by installing multiple sensors that capture different biometric traits. Such systems, known as multimodal biometric systems , are expected to be more reliable due to the presence of multiple pieces of evidence. These systems are also able to meet the stringent performance requirements imposed by various applications. For example, the feature extraction module of a fingerprint authentication system may be unable to extract features from fingerprints associated with specific individuals, due to the poor quality of the ridges. In such instances, it is useful to acquire multiple biometric traits for verifying the identity. Multimodal systems also provide anti-spoofing measures by making it difficult for an intruder to spoof multiple biometric traits simultaneously.
Feature Extraction using EBGM and KDDA
Elastic Bunch Graph Matching (EBGM):
Face recognition using elastic bunch graph matching is based on recognizing novel faces by estimating a set of novel features using a...
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