Biometric template protection based on a cancelable convolutional neural network over iris and fingerprint
BIOMEDICAL SIGNAL PROCESSING AND CONTROL(2024)
摘要
Multimodal biometric systems have gained popularity for their enhanced recognition accuracy and resistance to attacks like spoofing. In this paper, we introduce a novel approach to safeguard multimodal biometric templates using a Cancelable Convolutional Neural Network (CCNN). Our method utilizes two biometric traits, the iris and fingerprint. Initially, features are extracted separately from these traits and then combined into a single feature vector. Subsequently, a CCNN is applied to reduce the size of this fused vector. Finally, the reduced vector is multiplied with a user-provided seed for enhanced cancelability. Evaluations on the Children Multimodal Biometric Database (CMBD), CASIA Iris V3, and FVC 2002 DB2 demonstrate that our method effectively balances user privacy and accuracy while maintaining a high level of precision. With an exceptionally low Equal Error Rate (EER) of 0.073% and 0.038% on both datasets. Our method fulfills the requirements of diversity, irreversibility, and revocability showcasing its efficiency in terms of security and accuracy.
更多查看译文
关键词
Multimodal biometrics,Cancelability,Convolutional neural network,Iris,Fingerprint,Authentication system
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要