Abstract
人脸表情识别是人工智能与智能化人机交互中的重要组成部分，受到广泛的关注，具有广阔的应用前景与发展空间。本文基于数据场的人脸特征提取方法，将能凸显人脸表情的数据点加以降维和简化，使用正态云模型完成定性与定量之间的转换，反映了认知过程中的模糊性和随机性，结合综合云与云变换的方法，最终获得整张人脸表情的数字特征与云图。通过实验证明该方法可以有效地突出人脸表情特征，定量化地得出人脸表情概念，为人脸表情识别与判断开拓了新的思路和定量依据。As an important part of the technology for human, facial expression recognition has drawn much attention recently and has broad application prospects. The paper presents a conception of “Data Field” to extract facial feature points, then uses normal cloud model to complete the translation between qualitative and quantitative, reflecting the cognitive process fuzziness and randomness, combined with comprehensive cloud and cloud transformation method, got high-level concept, and got facial expression’s digital features and cloud model. Experiments show that this method can effectively highlight the characteristics of facial expression, and then conclude general cognitive concept and this method provides a new way of thinking on facial expression recognition.
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