Accurate and Robust Face Analysis: Collaborative Detection and Recognition via Meta-Auxiliary Learning Enhancement

The performance of face recognition is highly dependent on detection accuracy and robustness. However, most face recognition methods assume that face regions are perfectly aligned and rely exclusively on visual features within bounding boxes. This assumption is often violated in complex environments, including low resolution, poor illumination, and partial occlusion. To overcome these limitations, this paper proposes a collaborative face detection and recognition framework enhanced by meta-auxiliary learning. The proposed method exploits shared representations by an improved feature extraction module, and introduces gender classification and age estimation as auxiliary tasks to construct a meta-composite loss. Furthermore, facial geometric structure are acquired by extracting facial landmark coordinates from detected face regions. This multi-task architecture imposes implicit constraints on shared features, while simultaneously boosting feature discriminability and system robustness. By fusing facial geometric structures with semantic meta-information, facial identity representations are effectively enriched, leading to substantial performance gains in complex scenarios. Extensive experiments on the SCFace and LRD benchmark datasets validate the effectiveness of the proposed method. On SCFace, our method achieves 77.21% Genuine Acceptance Rate (GAR) at a False Acceptance Rate (FAR) of 1%, surpassing ArcFace, Octuplet Loss, and HDFGD by 1–2 percentage points. On LRD200 and LRD100 under an extreme low-resolution condition of 15 pixels, the recognition accuracies of the proposed method reach 67.98% and 60.98%, respectively—on par with state-of-the-art methods and markedly superior to conventional baselines. These results confirm that meta-auxiliary learning effectively compensates for insufficient visual information by leveraging semantic cues. Non-parametric statistical tests (Wilcoxon signed-rank test) confirm that the improvements achieved by the proposed method are statistically significant (p < 0.05).