Accurate analysis and classification of facial attributes are essential in various applications, from human-computer interaction to security systems. In this work, a novel approach to enhance facial classification and recognition tasks through the integration of 3D facial models with deep learning methods was proposed. We extract the most useful information for various tasks using the 3D Facial Model, leading to improved classification accuracy. Combining 3D facial insights with ResNet architecture, our approach achieves notable results: 100% individual classification, 95.4% gender classification, and 83.5% expression classification accuracy. This method holds promise for advancing facial analysis and recognition research.

通过将三维面部模型与深度学习方法相结合，本研究提出了一种新的方法来增强面部分类和识别任务，从而精确分析和分类面部属性。使用三维面部模型提取各种任务的最有用信息，从而提高了分类准确性。将三维面部洞察力与ResNet结构相结合，本方法取得了显著的结果：100％个体分类准确性，95.4％性别分类准确性和83.5％表情分类准确性。这种方法有望推进面部分析和识别的研究。

基于三维面部模型和深度学习的人脸分类与识别增强