Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation

Facial emotion recognition (FER) is a fine-grained problem where the value of transfer learning is often assumed. We first quantify this assumption and show that, on AffectNet, training from random initialization with sufficiently strong augmentation consistently matches or surpasses fine-tuning from ImageNet. Motivated by this result, we propose Hybrid Multi-Task Learning (HMTL) for FER in the wild. HMTL augments supervised learning (SL) with self-supervised learning (SSL) objectives during training, while keeping the inference-time model unchanged. We instantiate HMTL with two tailored pretext tasks, puzzling and inpainting with a perceptual loss, that encourage part-aware and expression-relevant features. On AffectNet, both HMTL variants achieve state-of-the-art accuracy in the eight-emotion setting without any additional pretraining data, and they provide larger gains under low-data regimes. Compared with conventional SSL pretraining, HMTL yields stronger downstream performance. Beyond FER, the same strategy improves fine-grained facial analysis tasks, including head pose estimation and gender recognition. These results suggest that aligned SSL auxiliaries are an effective and simple way to strengthen supervised fine-grained facial representation without adding extra computation cost during inference time.