This paper presents an EfficientNetV2-based framework for multi-label facial emotion recognition tailored to Korean facial expressions. Unlike traditional single-label models trained on Western datasets, our method embraces the complexity of co-occurring emotions by modeling them as concurrent affective states. Utilizing a dataset of 38,817 annotated images from Korean dramas, we fine-tune a deep network with gradual decoding layers and balanced data augmentation for multi-label facial emotion classification. Experimental results show state-of-the-art performance under various thresholds, achieving 95.80% accuracy and 97.27% F1-score at threshold = 0.5, significantly surpassing baseline models. The proposed framework offers a robust and culturally aware solution for affect-aware AI systems operating in socially rich environments.

Recent 3D face editing methods using masks have produced high-quality edited images by leveraging Neural Radiance Fields (NeRF). Despite their impressive performance, existing methods often provide limited user control due to the use of pre-trained segmentation masks. To utilize masks with a desired layout, an extensive training dataset is required, which is challenging to gather. We present FFaceNeRF, a NeRF-based face editing technique that can overcome the challenge of limited user control due to the use of fixed mask layouts. Our method employs a geometry adapter with feature injection, allowing for effective manipulation of geometry attributes. Additionally, we adopt latent mixing for tri-plane augmentation, which enables training with a few samples. This facilitates rapid model adaptation to desired mask layouts, crucial for applications in fields like personalized medical imaging or creative face editing. Our comparative evaluations demonstrate that FFaceNeRF surpasses existing mask based face editing methods in terms of flexibility, control, and generated image quality, paving the way for future advancements in customized and high-fidelity 3D face editing. The code is available on the project-page.