K-MEANS BASED MULTIMODAL BIOMETRIC AUTHENTICATION USING FINGERPRINT AND FINGER KNUCKLE PRINT WITH FEATURE LEVEL FUSION
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In general, identification and verification are done by passwords, pin number, etc.,which are easily cracked by others. To overcome this issue, biometrics has been introduced as aunique tool to authenticate an individual person. Biometric is a quantity which consists ofindividual physical characteristics that provide more authentication and security than thepassword, pin number, etc. Nevertheless, unimodal biometric suffers from noise, intra classvariations, spoof attacks, non-universality and some other attacks. In order to avoid these attacks,the multimodal biometrics, i.e. a combination of more modalities is adapted. Hence this paper hasfocused on the integration of fingerprint and Finger Knuckle Print (FKP) with feature level fusion.The features of Fingerprint and (FKP) are extracted. The feature values of fingerprint usingDiscrete Wavelet Transform and the key points of FKP are clustered using K-Means clusteringalgorithm and their values are fused. The fused values of K-Means clustering algorithm is stored ina database which is compared with the query fingerprint and FKP K-Means centroid fused valuesto prove the recognition and authentication. The comparison is based on the XOR operation.Hence this paper provides a multimodal biometric recognition method to provide authenticationwith feature level fusion. Results are performed on the PolyU FKP database and FVC 2004fingerprint database to check the Genuine Acceptance Rate (GAR) of the proposed multimodalbiometric recognition method. The proposed multimodal biometric system provides authenticationand security using K-Means clustering algorithm with GAR=99.4%, FRR=0.6% and FAR=0%with security of 128 bits for each modality.
feature level fusion
fingerprint and FKP feature extraction
K-Means clustering algorithm
multimodal biometric systems