In the contemporary landscape of computer vision and affective computing, the ability of machines to perceive not only human identity but also psychological state is a cornerstone of advanced Human-Computer Interaction (HCI). While traditional surveillance and authentication systems focus exclusively on identity verification, they often ignore the contextual layer of human emotion, which is vital for applications ranging from personalized marketing to mental health monitoring. This research proposes a high-performance, integrated framework for simultaneous Real-Time Face Recognition and Emotion Detection. The system architecture employs a multi-stage computational pipeline: initial face localization is achieved via Haar Cascade Classifiers, identity recognition is processed through Local Binary Pattern Histograms (LBPH), and affective state classification is performed by a deep Convolutional Neural Network (CNN) optimized for real-time inference. Our proposed model tackles the limitations of high latency, cloud-based architectures by utilizing localized edge-processing, granting data privacy, and minimizing processing times. A hybrid dataset (an identity matching custom facial repository and an emotion classification FER-2013 benchmark dataset) was used to train and validate the model. The performance of the training set was recorded via stable frame rates (22 & 25 FPS) on common consumer-grade hardware, achieving recognition accuracy of 91.4% and emotion classification precision of 88.7% in the four core emotional states (Happiness, Sadness, Anger, and Neutrality). Consequently, we can use classical texture-based descriptors together with hierarchical deep learning features to form a solid and lightweight approach applicable in smart environments, interactive educational tools, and automated security protocols.
Face Recognition; Emotion Detection; Convolutional Neural Networks (CNN); Local Binary Pattern Histograms (LBPH); OpenCV; Real-Time Systems; Affective Computing; Human-Computer Interaction (HCI).
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