Facial Emotion Recognition with CNNs: Analyzing Accuracy and Demographic Factors

Facial emotion recognition (FER) has become a basic issue of research in computer vision and has major applications in fields including mental health analysis, human-computer interface, security, and customer experience. Since facial expressions change considerably depending on demographic diversity, lighting conditions, and personal expression variance, accurate detection of face emotions is challenging. In this work, we developed a deep learning-based FER model employing convolutional neural networks (CNNs) to classify seven different emotions: anger, contempt, disgust, fear, happiness, sorrow, and surprise. The dataset used in this work consists of images expressing these emotions spanning a broad spectrum of people in terms of age, gender, and ethnicity, therefore ensuring that the model can generalize well to real-world scenarios. To guarantee strong training and validation, our approach consists in meticulous dataset splitting and thorough data preparation. Over several epochs, the CNN architecture was trained to produce a final validation accuracy of 92.31%, therefore proving the model’s potency in emotional categorization challenges. Model performance was evaluated using precision (0.87), recall (0.92), and F1-score (0.89); most emotional categories showed great dependability from the results. This work shows the possibilities of CNN-based FER systems to be used in real-time applications, therefore laying a basis for further developments in emotion-aware artificial intelligence technology.