Efficient and High Performing Biometrics: Towards Enabling Recognition in Embedded Domains

Abstract

The growing need for reliable and accurate recognition solutions along with the recent innovations in deep learning methodologies has reshaped the research landscape of biometric recognition. Developing efficient biometric solutions is essential to minimize the required computational costs, especially when deployed on embedded and low-end devices. This drives the main contributions of this work, aiming at enabling wide application range of biometric technologies.

Towards enabling wider implementation of face recognition in use cases that are extremely limited by computational complexity constraints, this thesis presents a set of efficient models for accurate face verification, namely MixFaceNets. With a focus on automated network architecture design, this thesis is the first to utilize neural architecture search to successfully develop a family of lightweight face-specific architectures, namely PocketNets. Additionally, this thesis proposes a novel training paradigm based on knowledge distillation (KD), the multi-step KD, to enhance the verification performance of compact models. Towards enhancing face recognition accuracy, this thesis presents a novel margin-penalty softmax loss, ElasticFace, that relaxes the restriction of having a single fixed penalty margin.

Occluded faces by facial masks during the recent COVID-19 pandemic presents an emerging challenge for face recognition. This thesis presents a solution that mitigates the effects of wearing a mask and improves masked face recognition performance. This solution operates on top of existing face recognition models and thus avoids the high cost of retraining existing face recognition models or deploying a separate solution for masked face recognition.

Aiming at introducing biometric recognition to novel embedded domains, this thesis is the first to propose leveraging the existing hardware of head-mounted displays for identity verification of the users of virtual and augmented reality applications. This is additionally supported by proposing a compact ocular segmentation solution as a part of an iris and periocular recognition pipeline. Furthermore, an identity-preserving synthetic ocular image generation approach is designed to mitigate potential privacy concerns related to the accessibility to real biometric data and facilitate the further development of biometric recognition in new domains.