Exploring Deep Learning Ear Recognition in Thermal Images

Ear biometrics is an appealing choice for human recognition in various daily activities, ranging from surveillance to commercial-related applications. It has multiple advantages in identifying non-cooperative individuals in visible unconstrained environments. Moreover, Infrared imagery is an alternative sensing modality for ear recognition systems in the dark or when there is no control over illumination. In this work, we investigate the performance of deep learning models for ear recognition in the visible and thermal infrared domains. We examine multiple convolutional neural network architectures and develop different strategies to enhance learning. We use a dual-band dataset for our experiments, recently collected for multi-pose (full frontal to full profile) face recognition applications. We also use other ear datasets for learning, the AWE visible Ear Dataset & the WVU MIWR Profile Face Dataset, for visible domain and thermal domain learning, respectively. Our experiments achieve a 98.76% Rank-1 identification accuracy, 0.7596% Equal Error Rate (EER) verification for the visible domain ear recognition, and 96.93% Rank-1 identification accuracy, 2.4541% EER verification for the thermal domain ear recognition.