Fusion of iris and palmprint for multimodal biometric authentication

Abstract

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.

Composite Feature Extraction and Classification for Fusion of Palm-Print and Iris Biometric Traits

“…The fusion system accuracy was 99.2% by using the KNN classifier. Authors in [9] presented a multimodal of iris and palm-print by extracting global features using wavelet transform. Haar wavelet transform was used to extract iris features at four levels.…”

The Framework Design for Increasing Security of Multi-Modal Biometric Authentication System With DNN

“…In real time example or application of multi-modal biometric can be in the security of ATM. The security is the major concept in ATM because PIN number could be easily hacked [2].…”