Multimodal Biometrics at Feature Level Fusion Using Texture Features

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Provided by: Computer Science Journals
Topic: Security
Format: PDF
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from curvelet transform. The curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal Component Analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and chimeric databases.
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