Emotion Recognition from Facial Expressions Using Convolutional Neural Networks

Facial expressions are one of the most natural and universal ways of conveying human emotions, making their automatic recognition a critical component in affective computing and human–computer interaction. This paper presents a Convolutional Neural Network (CNN)-based approach for emotion recognition from facial images. The proposed model utilizes deep feature extraction to capture spatial hierarchies in facial regions, enabling accurate classification of emotions such as happiness, sadness, anger, surprise, fear, disgust, and neutrality. By training and evaluating the CNN on publicly available benchmark datasets, the model demonstrates robust performance and generalization across diverse facial variations. Experimental results highlight the efficiency of CNNs in recognizing subtle emotional cues without relying on handcrafted features. The proposed approach holds significant potential for applications in mental health monitoring, intelligent tutoring systems, adaptive user interfaces, and surveillance systems where understanding human emotions is essential.

Affective Computing, Human–Computer Interaction, Deep Learning, Convolutional Neural Networks, Emotion Recognition, Facial Expression Recognition

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