Adaptive Anchor Matching Strategy for Face Detection

Abstract

Face detection is a fundamental task for numerous face-related applications (e.g., face recognition and age estimation), which directly affects the performance of the subsequent processing. Recent anchor-based face detectors have demonstrated the great potential by matching anchors and target boxes during training, which is crucial for high performance and training efficiency. However, existing anchor matching strategies still suffer from: 1) ignoring the inherent relationship between the targets and the anchors, which may cause unsuitable matched pairs, 2) adopting a fixed matching threshold, which cannot meet the varying demands of matched pairs for quality and quantity in different feature levels and training processes, and 3) the heuristic anchor setting, whose matching range is too narrow to capture about 20% target faces in the training dataset. This paper proposes an Adaptive Anchor Matching Strategy (AAMS) to address these issues, which selectively assigns proper targets to anchors in different feature levels by using adaptive matching thresholds and a robust anchor setting determined by the statistical characteristic of the training samples. Extensive experiments on popular benchmarks reveal that the proposed approach has significant improvements on anchor-based models and outperforms the recent state-of-the-arts methods in terms of both accuracy and generalization.