Facial expression detection technology is a flexible tool utilized in many different industries that allows for a better understanding of human behavior and emotional responses. Its applications span a broad spectrum of societal requirements, from enhancing marketing strategies to bolstering security protocols and assisting in the diagnosis of mental health issues. By using the power of artificial intelligence and computer vision, facial expression recognition (FER) helps businesses to adapt, grow and build deeper interactions between people and the technology and environments they live in. Its primary goal remains to facilitate better understanding, communication and decision-making in order to bring in a new wave of technical innovation that is driven by people’s needs. Facial emotion recognition, which makes use of computer vision and artificial intelligence, not only improves the capacity to understand and react to human emotions but also opens the door to more personalized and empathic interactions in an increasingly digital environment. In this paper, unique method of facial emotion recognition based on sophisticated feature extraction techniques combined with a hybrid ensemble learning technique. In particular, hierarchical cascade regression neural networks (HCRNN) for face landmark identification, VGG-19 for feature extraction and Random Forests are considered as classification techniques. In order to accomplish reliable and accurate emotion recognition from facial images, this method takes advantage of the complementary capabilities of both approaches. Initially, we utilized a deep convolutional neural network (CNN) called VGG-19 to extract high-level features from face images. Pre-trained on extensive image datasets, VGG-19 has proven to be highly effective at capturing complex visual representations. These characteristics function as an all-inclusive description of facial expressions, encompassing both general trends and minute details. HCRNN for facial landmark detection, which precisely locates important face features including the mouth, nose and eyes. Facial landmark predictions are iteratively improved by this hierarchical cascade architecture, which also handles occlusions and changes in illumination and position. Lastly, we use an ensemble learning technique called Random Forests to classify emotions. Multiple decision trees’ predictions are combined by random forests, which offer robustness against overfitting and efficiently handle high-dimensional feature fields. High accuracy and generalization capabilities are achieved in the facial emotion classification process by Random Forests, which combine the feature representations retrieved by VGG-19 with the localized facial landmarks recognized by HCRNN. We tested the suggested strategy on benchmark face emotion detection datasets and compared its results with the most advanced techniques available. Performance measures like accuracy of 98.89%, precision 96.52%, recall 96%, F1-score 99% and receiver operating characteristic (ROC) curve of 97.24% analysis were used to evaluate how well hybrid strategy worked and how much better it was at identifying emotions in facial images. The outcomes of experiments show how method has practical applications in human–computer interaction, healthcare and other fields.
