Device-Specific Facial Descriptors: Winning a Lottery with a SuperNet

Abstract

We address the challenge of devising neural network architectures to extract facial descriptors across diverse mobile and edge devices. Employing neural architecture search, we introduce a novel framework that selects optimal subnetworks from a SuperNet using an evolutionary search. Using a surrogate gradient boosting classifier to avoid direct accuracy estimation of subnetworks on validation sets, our approach swiftly delivers the most efficient and accurate models tailored to specific devices within minutes. Demonstrating versatility through an Android demo app, our framework excels in tasks like face recognition and emotion understanding across various devices, achieving real-time processing and superior accuracy compared to existing mobile models.