In order to give consumers more individualized experiences, this study presents an innovative new technology that blends artificial intelligence with the ability to recognize human emotions. The method seeks to improve emotional well-being by identifying emotions in real-time and recommending comfort foods. The method is very thorough, linking food preferences to emotional states using data analysis, computer vision, and natural language processing (NLP). In order to identify the user's prevailing emotion, it analyzes emotions using the Deep Face library and detects faces using the Haar Cascade Classifier. It does this by analyzing facial expressions from a live video feed. For example, the algorithm may propose comforting foods like chocolate if someone looks depressed, and celebration delicacies like cakes if they appear cheerful. A comprehensive collection of comfort foods and the emotions they arouse serves as the foundation for this emotional mapping. To identify faces and emotions, the procedure starts with information from a webcam and analyzes it in real time. To guarantee precise mapping, the NLP component cleans up the data. The recommendation engine's capacity to rate comfort meals according to consumers' emotional associations makes it stand out and guarantees that the suggestions are pertinent and meaningful. With a high user satisfaction rate and an emotion recognition accuracy of almost 90%, this scalable and lightweight system shows how AI may be used to address emotional demands and enhance human-computer interactions.
Real-time Emotion Detection, Comfort Food Recommendation, DeepFace, Haar Cascade Classifier, Natural Language Processing, AI-based Personalization, User-Centric Design.
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