| 舒锐,彭挺,傅铭伟.融合表情识别技术与人工智能模型的书籍推荐智能体[J].电子测量与仪器学报,2026,40(4):278-288 |
| 融合表情识别技术与人工智能模型的书籍推荐智能体 |
| Book recommendation agent based on expression recognitiontechnology and artificial intelligence model |
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| DOI： |
| 中文关键词: 表情识别 人工智能 智能体 MTCNN MobileNet_V2 |
| 英文关键词:expression recognition artificial intelligence agent MTCNN MobileNet_V2 |
| 基金项目:四川省第一批现场工程师专项培养计划项目（教职成厅函〔2023〕6号）、西南交通大学希望学院实践示范课程建设项目（SJSF2025006）资助 |
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| Author | Institution |
| Shu Rui | Department of Mechanical, Electrical and Rail Transit Engineering, Hope College of
Southwest Jiaotong University, Chengdu 610400, China |
| Peng Ting | Department of Mechanical, Electrical and Rail Transit Engineering, Hope College of
Southwest Jiaotong University, Chengdu 610400, China |
| Fu Mingwei | Department of Mechanical, Electrical and Rail Transit Engineering, Hope College of
Southwest Jiaotong University, Chengdu 610400, China |
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| 中文摘要: |
| 随着人工智能模型的飞速发展，合理的开发和利用人工智能模型服务人们生活变得十分重要。鉴于传统推荐系统无法感知用户实时情绪，难以提供情绪适配书籍的问题，针对这一问题，设计了一种融合表情识别技术与人工智能模型的书籍推荐智能体。为了使表情识别结果更加精确，创新性地采用MTCNN-MobileNet_V2 融合架构进行训练，训练结果显示此方法相较于传统基于HOG的识别方法平均准确率提高约23.6%。该智能体基于树莓派4B硬件平台，首先通过Camera V2实时采集用户面部图像，采用MTCNN算法检测并对齐人脸区域，再使用MobileNet_V2轻量化卷积神经网络进行表情分类；随后，系统集成大语言模型Qwen-3-32B进行语义推理和生成个性化书籍推荐；最后利用MQTT与Home Assistant实现推荐结果实时展示。实验结果表明，此系统平均准确率为94.72%，在标准光照（≈800 Lux）下情绪识别准确率为97.7%；在低照度（≈350 Lux）下为83.1%。5人场景下识别准确率约92%，响应时延约5 s，系统响应延迟适中，可实现实时推荐。该智能体显著提升了推荐准确度和用户体验，同时验证了此方法的有效性，为未来基于情感输入的个性化推荐提供了有效依据。 |
| 英文摘要: |
| With the rapid development of artificial intelligence models, the reasonable development and utilization of these models to serve people’s daily lives have become increasingly important. Given that traditional recommendation systems are unable to perceive users’ real-time emotions and therefore struggle to provide emotion-aligned book recommendations, this study proposes an intelligent book recommendation agent that integrates facial expression recognition technology with artificial intelligence models. In order to make the expression recognition results more accurate, this paper innovatively uses MTCNN-MobileNet_V2 fusion architecture for training. The training results show that the average accuracy of this method is about 23.6% higher than that of the traditional HOG based recognition method. The agent is based on the Raspberry Pi 4B hardware platform. Firstly, the user’s face image is collected in real time through Camera V2, and the face region is detected and aligned using MTCNN algorithm. Then, the expression classification is performed using MobileNet_V2 lightweight convolutional neural network; Then, the system integrates the large language model Qwen-3-32B to perform semantic reasoning and generate personalized book recommendations; Finally, MQTT and Home Assistant are used to realize the real-time display of recommendation results. The experimental results show that the average accuracy of this system is 94.72%, and the accuracy of emotion recognition is 97.7% under standard light (≈800 Lux); 83.1% under low illumination (≈350 lux). In the five person scene, the recognition accuracy is about 92%, the response delay is about 5 seconds, and the system response delay is moderate, which can realize real-time recommendation. The agent significantly improves the recommendation accuracy and user experience, and verifies the effectiveness of this method, which provides an effective basis for personalized recommendation based on emotional input in the future. |
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