Biometric template protection based on a cancelable convolutional neural network over iris and fingerprint

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.