Recognizing facial expressions is a major challenge and will be required in the latest fields of research such as the industrial Internet of Things. Currently, the available methods are useful for detecting singular facial images, but they are very hard to extract. The main aim of face detection is to capture an image in real-time and search for the image in the available dataset. So, by using this biometric feature, one can recognize and verify the person’s image by their facial features. Many researchers have used Principal Component Analysis (PCA), Support Vector Machine (SVM), a combination of PCA and SVM, PCA with an Artificial Neural Network, and even the traditional PCA-SVM to improve face recognition. PCA-SVM is better than PCA-ANN as PCA-ANN has the limitation of a small dataset. As far as classification and generalization are concerned, SVM requires fewer parameters and generates less generalization errors than an ANN. In this paper, we propose a new framework, called FRS-DCT-SVM, that uses GA-RBF for face detection and optimization and the discrete cosine transform (DCT) to extract features. FRS-DCT-SVM using GA-RBF gives better results in terms of clustering time. The average accuracy received by FRS-DCT-SVM using GA-RBF is 98.346, which is better than that of PCA-SVM and SVM-DCT (86.668 and 96.098, respectively). In addition, a comparison is made based on the training, testing, and classification times.