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.

翻译：面部属性的精确分析与分类在人机交互到安全系统等多个领域具有关键作用。本研究提出了一种通过将3D面部模型与深度学习方法相结合来增强面部分类与识别任务的新方法。我们利用3D面部模型提取各任务中最有价值的信息，从而提升分类精度。通过将3D面部特征与ResNet架构相融合，该方法取得了显著成果：个体识别准确率达100%，性别分类准确率达95.4%，表情分类准确率达83.5%。该方法为推进面部分析与识别研究提供了重要前景。