An Intelligent Smart Mirror Framework for Real-Time Emotion Awareness and LLM-Based Reflective Guidance

Abstract

Recognizing and interpreting emotional states in real time remains a challenge for individuals seeking to improve psychological resilience and emotional self-awareness. This paper presents Istibtan, a human-centered mobile framework designed to support reflective emotional awareness through real-time facial expression recognition (FER) and personalized feedback.The system employs a Convolutional Neural Network (CNN) trained on the FER-2013 dataset, augmented with facial landmark detection to enhance robustness under varying environmental conditions. Beyond emotion classification, Istibtan distinguishes itself through the integration of large language models (Gemini LLMs), which transform raw affective data into context-aware, psychology-informed reflective guidance, supporting meaningful user interpretation rather than passive monitoring.Developed using the Flutter SDK with secure cloud-based data management, the application further supports sustained emotional engagement through digital journaling and longitudinal emotion tracking modules. Evaluation on external validation datasets achieved a classification accuracy of 76.86%, indicating reliable generalization performance.By combining affective computing, reflective interaction design, and AI-driven personalization, this work contributes a novel mobile framework for enhancing emotional intelligence and proactive mental well-being, with clear implications for human-centered mental health technologies.

Keywords: Facial Expression Recognition, Affective Computing, Large Language Models (LLM)

DOI: 10.54941/ahfe1007348