HZS-NAS: Neural Architecture Search with Hybrid Zero-Shot Proxy for Facial Expression Recognition

Facial expression recognition (FER) involves analyzing and interpreting human sentiment from facial data, which has been an active topic in human-computer interaction. In recent years, convolutional neural networks (CNNs) have been intensively studied for FER tasks. Unfortunately, high-accuracy FER models typically contain numerous parameters, making them unsuitable for deployment on resource-constrained devices. Designing lightweight models that strike a balance between model complexity and accuracy is challenging. Neural architecture search (NAS) is a promising approach to automatically achieve the balance. However, previous NAS-based works required heavy training in supernet or intensive network evaluations, making the search process expensive. In this paper, we propose a NAS algorithm with a hybrid zero-shot (ZS) proxy for FER tasks, which can efficiently search for lightweight FER networks with high accuracy. To the best of our knowledge, the paper is the first to use zero-shot neural architecture search on FER tasks. Specifically, a novel performance evaluation strategy with the efficient hybrid ZS proxy is proposed, which can evaluate the accuracy of a single FER network in seconds. In addition, a hierarchical search space for FER tasks is designed to encourage the diversity of FER networks, which significantly affects recognition accuracy. Experimental results show that the proposed method consistently achieves excellent results in terms of accuracy and the number of parameters across public FER datasets (FER2013 and RAF-DB). Moreover, the proposed method takes much less time than other state-of-the-art NAS-based FER algorithms.