'Blinks in the Dark': Blink Estimation With Domain Adversarial Training (BEAT) Network

Abstract

Blink detection plays an important role in many human-computer interaction applications for consumers. Unfortunately, deep neural network-based blink detection methods are not only susceptible to poor lighting conditions, but also the deep learning model is prone to bias due to the imbalance in the dataset distribution. To solve these problems, we propose Blink Estimation with Domain Adversarial Training (BEAT) network, which robustly detects blinks in unseen out-of-sample images captured even under poor lighting conditions by extracting domain-invariant features. BEAT network is inspired by the domain-adversarial neural network (DANN) but improved with several improvements including a lambda scheduler to stabilize adversarial training and a gradient decay layer to prevent the discriminative loss from overwhelming the classification loss. As a result, BEAT achieves faster and more accurate blink detection performances than other domain generalization methods for unseen target domains. In particular, BEAT's feature extractor model achieves state-of-the-art performance in terms of AUPR on popular benchmark datasets. Also, we suggest a practical optimal threshold for blink detection based on our insights gained from our experiments for consumer applications.