A cancelable ear recognition system via optimized deep feature fusion

Abstract The rapid expansion of biometric authentication technologies worldwide has heightened the need for highly reliable and secure identification methods. This research explores the human ear as a distinctive biometric trait, capitalizing on its stable and person-specific anatomical structure. Although ear biometrics offer notable advantages, their practical use is hindered by image variations arising from changes in pose, scale, rotation, illumination, and contrast. To overcome these challenges, this paper presents an innovative deep learning-based ear recognition framework. The proposed approach employs a dual-stream feature extraction strategy that integrates two advanced Convolutional Neural Network (CNN) models MobileNetV3 and DenseNet-121 to derive rich and complementary feature representations, which are subsequently fused. The resulting high-dimensional feature space is then optimized using a Multi-Learning Strategy Golden Eagle Optimization (MLSGEO) algorithm to retain only the most discriminative features. To strengthen security and privacy, the refined feature vector is transformed into a non-invertible, cancelable biometric template using a Comb-filter–based protection mechanism. Data augmentation techniques are further applied to compensate for dataset size limitations. The framework was evaluated on five benchmark ear datasets: AMI, AWE, IITD-I, IITD-II, and UERC, achieving recognition accuracies of 99.90%, 99.64%, 99.78%, 99.32%, and 93.31%, respectively. Experimental findings show that the proposed system outperforms existing state-of-the-art methods. Overall, the integration of robust feature learning with a resilient template protection scheme demonstrates strong potential for secure and high-accuracy biometric authentication applications.