Optimizing Face Recognition and Emotion Detection in Student Identification Using FaceNet and YOLOv8 Models
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Abstract
The growing need for efficient student identification systems has driven advancements in face recognition and emotion detection technologies. This research presents a single-photo-based system that integrates YOLOv8 for face detection and FaceNet for generating unique facial embeddings, ensuring high-precision student identification and consistent emotion detection under diverse conditions. YOLOv8 localizes faces within images, while FaceNet processes them to generate embeddings for recognition. Emotion detection is performed using these embeddings or an auxiliary emotion classification model. The methodology includes pre-processing images into 64x64 grayscale format, employing image augmentation to enhance model generalization, and evaluating performance using accuracy, precision, recall, and F1 score metrics. The experimental dataset comprises 10 formal student photos, with testing conducted on 100 images. Results demonstrate 94% accuracy in face recognition with augmentation, surpassing 92% without it. Emotion detection achieves 95% accuracy in identifying seven emotions, including angry, happy, sad, neutral, fear, disgust, and surprise, despite variations in expression, lighting, and angle. This system provides a scalable and efficient solution for educational applications such as automated student identification, attendance monitoring, and emotion-based learning management. Its potential spans short-term automation in attendance and mental health monitoring, medium-term improvements in personalized learning and campus security, and long-term AI-driven educational advancements while addressing privacy and social acceptance challenges.
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