随着计算机硬件和通信技术的进步,深度学习技术取得了重大进展,能够开发能够准确估计人类情绪的系统。面部表情等因素,性别,年龄,环境影响人类的情感,理解和捕捉这些复杂因素至关重要。我们的系统旨在通过准确估计人类情绪来推荐个性化图像,年龄,和实时的性别。我们系统的主要目标是通过推荐与其当前情绪状态和特征一致的图像来增强用户体验。为了实现这一点,我们的系统收集环境信息,通过API和智能手机传感器,包括天气状况和用户特定的环境数据。此外,我们采用深度学习算法对八种面部表情进行实时分类,年龄,和性别。通过将这些面部信息与环境数据相结合,我们将用户的当前情况分为积极的,中性,消极阶段。基于这种分类,我们的系统推荐使用生成对抗网络(GAN)着色的自然景观图像。这些建议是个性化的,以匹配用户的当前情绪状态和偏好,提供更具吸引力和量身定制的体验。通过严格的测试和用户评估,我们评估了我们系统的有效性和用户友好性。用户对系统根据周围环境生成适当图像的能力表示满意,情绪状态,以及年龄和性别等人口因素。我们系统的视觉输出显著影响了用户的情绪反应,为大多数用户带来积极的情绪变化。此外,系统的可扩展性得到了积极的接收,用户承认其在户外安装时的潜在好处,并表示愿意继续使用它。与其他推荐系统相比,我们的年龄整合,性别,天气信息提供个性化建议,上下文相关性,增加参与度,更深入地了解用户偏好,从而增强整体用户体验。系统理解和捕捉影响人类情绪的复杂因素的能力在各个领域都有希望,包括人机交互,心理学,和社会科学。
With the advancement of computer hardware and communication technologies, deep learning technology has made significant progress, enabling the development of systems that can accurately estimate human emotions. Factors such as facial expressions, gender, age, and the environment influence human emotions, making it crucial to understand and capture these intricate factors. Our system aims to recommend personalized images by accurately estimating human emotions, age, and gender in real time. The primary objective of our system is to enhance user experiences by recommending images that align with their current emotional state and characteristics. To achieve this, our system collects environmental information, including weather conditions and user-specific environment data through APIs and smartphone sensors. Additionally, we employ deep learning algorithms for real-time classification of eight types of facial expressions, age, and gender. By combining this facial information with the environmental data, we categorize the user\'s current situation into positive, neutral, and negative stages. Based on this categorization, our system recommends natural landscape images that are colorized using Generative Adversarial Networks (GANs). These recommendations are personalized to match the user\'s current emotional state and preferences, providing a more engaging and tailored experience. Through rigorous testing and user evaluations, we assessed the effectiveness and user-friendliness of our system. Users expressed satisfaction with the system\'s ability to generate appropriate images based on the surrounding environment, emotional state, and demographic factors such as age and gender. The visual output of our system significantly impacted users\' emotional responses, resulting in a positive mood change for most users. Moreover, the system\'s scalability was positively received, with users acknowledging its potential benefits when installed outdoors and expressing a willingness to continue using it. Compared to other recommender systems, our integration of age, gender, and weather information provides personalized recommendations, contextual relevance, increased engagement, and a deeper understanding of user preferences, thereby enhancing the overall user experience. The system\'s ability to comprehend and capture intricate factors that influence human emotions holds promise in various domains, including human-computer interaction, psychology, and social sciences.