基于Python Django的人脸表情分类算法与数据库论文

本文介绍了一款基于Python Django框架开发的人脸表情识别系统。在当今数字化的信息时代，人脸识别技术在信息验证、密码安全等领域得到广泛应用。人脸识别技术已存在几十年，研究人员在人脸轮廓识别等方面积累了丰富的经验。本文针对面部表情的识别进行了深入研究，通过提取面部特征位置，如眉毛、眼睛、脸型等，构建面部总体特征，实现对高兴、愤怒、悲伤等多种表情的有效识别。 本文采用分类网络来构建样本，并通过深度学习和训练制作出相关的识别模型。系统通过分类操作对人脸表情进行有效分类，从而实现准确的人脸表情识别。具体开发过程中，使用了Python编程语言和Django框架技术。 关键词：人脸表情识别、Python、Django、人脸特征 Abstract: In the current data-based information age, various aspects of people's daily lives are covered by digitalization. Among the many digital applications, face recognition is a mature technology that is widely used in information verification, password security, etc. Face recognition technology has existed for several decades, and researchers have accumulated a wealth of research experience in basic face contour recognition and so on. There has also been in-depth research on facial expression recognition, which extracts features based on facial features such as eyebrows, eyes, and face shape, to achieve facial recognition confirmation. A more accurate face confirmation is achieved by using script features to construct the overall features of the face. This paper develops a facial expression recognition system using the Django framework technology. The system can effectively recognize various expressions such as joy, anger, and sadness made by human beings. In this recognition, a classification network is used to construct samples. Through deep learning and training of the computer, relevant recognition models are created. The system then performs effective face expression classification through the classification operation, thus achieving the application of effective facial expression recognition. Keywords: Facial expression recognition; Python; Django; Facial features