用遗传算法优化生成情绪深度人脸，提升多样性与质量。
Synthetic Data Generation for Emotional Depth Faces: Optimizing Conditional DCGANs via Genetic Algorithms in the Latent Space and Stabilizing Training with Knowledge Distillation
- 用遗传算法在隐空间进化潜变量，增强目标情绪的多样性
- 结合知识蒸馏稳定训练，生成图像质量显著优于主流方法
- 适合情感计算、虚拟角色生成等需要真实情绪数据的场景
情感计算面临重大挑战：缺乏高质量、多样化的深度面部数据集以识别细微情绪表达。本文提出一种基于优化GAN的合成深度人脸生成框架，通过知识蒸馏（EMA教师模型）稳定训练，提升生成质量并防止模式崩溃。同时，采用遗传算法基于图像统计特性演化生成潜变量，显著提升目标情绪下的多样性与视觉质量。该方法在多样性与质量上均优于GAN、VAE、GMM和KDE。分类任务中，提取并拼接LBP、HOG、Sobel边缘与强度直方图特征，使用XGBoost实现94%和96%的准确率。基于FID、IS、SSIM和PSNR的评估显示，性能持续领先现有最佳方法。
原文摘要 · Abstract (English)
Affective computing faces a major challenge: the lack of high-quality, diverse depth facial datasets for recognizing subtle emotional expressions. We propose a framework for synthetic depth face generation using an optimized GAN with Knowledge Distillation (EMA teacher models) to stabilize training, improve quality, and prevent mode collapse. We also apply Genetic Algorithms to evolve GAN latent vectors based on image statistics, boosting diversity and visual quality for target emotions. The approach outperforms GAN, VAE, GMM, and KDE in both diversity and quality. For classification, we extract and concatenate LBP, HOG, Sobel edge, and intensity histogram features, achieving 94% and 96% accuracy with XGBoost. Evaluation using FID, IS, SSIM, and PSNR shows consistent improvement over state-of-the-art methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。