Facial Expression Recognition Through Transfer Learning: Integration of VGG16, ResNet, and AlexNet with a Multiclass Classifier

This study investigates the recognition of seven primary human emotions-contempt, anger, disgust, surprise, fear, happiness, and sadness-based on facial expressions.A transfer learning approach was employed, utilizing three pre-trained convolutional neural network (CNN) architectures: AlexNet, VGG16, and ResNet50.The system was structured to perform facial expression recognition (FER) by incorporating three key stages: face detection, feature extraction, and emotion classification using a multiclass classifier.The proposed methodology was designed to enhance pattern recognition accuracy through a carefully structured training pipeline.Furthermore, the performance of the transfer learning models was compared using a multiclass support vector machine (SVM) classifier, and extensive testing was planned on large-scale datasets to further evaluate detection accuracy.This study addresses the challenge of spontaneous FER, a critical research area in human-computer interaction, security, and healthcare.A key contribution of this study is the development of an efficient feature extraction method, which facilitates FER with minimal reliance on extensive datasets.The proposed system demonstrates notable improvements in recognition accuracy compared to traditional approaches, significantly reducing misclassification rates.It is also shown to require less computational time and resources, thereby enhancing its scalability and applicability to real-world scenarios.The approach outperforms conventional techniques, including SVMs with handcrafted features, by leveraging the robust feature extraction capabilities of transfer learning.This framework offers a scalable and reliable solution for FER tasks, with potential applications in healthcare, security, and human-computer interaction.Additionally, the system's ability to function effectively in the absence of a caregiver provides significant assistance to individuals with disabilities in expressing their emotional needs.This research contributes to the growing body of work on facial emotion recognition and paves the way for future advancements in artificial intelligence-driven emotion detection systems.