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A practical eye authentication (EA) system targeted for edge devices needs toperform authentication and be robust to presentation attacks, all whileremaining compute and latency efficient. However, existing eye-based frameworksa) perform authentication and Presentation Attack Detection (PAD) independentlyand b) involve significant pre-processing steps to extract the iris region.Here, we introduce a joint framework for EA and PAD using periocular images.While a deep Multitask Learning (MTL) network can perform both the tasks, MTLsuffers from the forgetting effect since the training datasets for EA and PADare disjoint. To overcome this, we propose Eye Authentication with PAD(EyePAD), a distillation-based method that trains a single network for EA andPAD while reducing the effect of forgetting. To further improve the EAperformance, we introduce a novel approach called EyePAD++ that includestraining an MTL network on both EA and PAD data, while distilling the`versatility' of the EyePAD network through an additional distillation step.Our proposed methods outperform the SOTA in PAD and obtain near-SOTAperformance in eye-to-eye verification, without any pre-processing. We alsodemonstrate the efficacy of EyePAD and EyePAD++ in user-to-user verificationwith PAD across network backbones and image quality.

EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images