Ocular biometrics refers to the use of features of the eye for person recognition. For instance, the unique and stable texture of the iris has been recognised as a powerful ocular biometric characteristic. In this study, the authors propose to improve biometric authentication with a multimodal ocular biometric system based on the iris pattern and the three-dimensional shape of the cornea. They show how the cornea can be used as a biometric trait for person recognition and then, they propose an intra-ocular fusion with iris features to improve the overall performance of the system. Feature extraction was done by modelling the shape of the cornea with a Zernike polynomial expansion. Then the best linear combinations of Zernike coefficients were found with linear discriminant analysis and used as biometric identifier. The iris texture was analysed with a typical methodology using Gabor filtering and phase encoding. The fusion was performed at the matching score level using min, max, sum and weighted-sum rule. The experimental results on a new database constructed for this bi-modal study showed impressive performance of the proposed ocular biometric system with equal error rate decreasing to 0% with the weighted-sum rule.
This paper presents a multimodal biometric system for authentication, based on the fusion of iris and palmprint. We propose an approach for feature extraction of each modality by using wavelet packet decomposition at four levels. This gives 256 packets which can generate a compact binary code. It is obtained from the first three highest energy peaks to compute an adapted threshold that enable to affect 0 or 1 to each wavelet packet. Different fusion strategies were tested at different levels: feature level, score level and error level. The first fusion is a simple concatenation of iris and palmprint codes. The second employs a weighted sum rule to matching scores. The third applies the Hamacher t-norm to the errors. The proposed approach and each fusion strategy were tested for their accuracy on the Casia iris database fused with the Casia palmprint database, and then with the PolyU database. The proposed approach for multimodal biometric system achieves a recognition improvement with each fusion method.
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