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Emotions expressed on a human face have a significant impact on decisions and arguments on a variety of topics. According to psychological theory, a person’s emotional states can be categorized as the afraid, disgusted, angry, sad, happy, neutral face and surprised. The automatic extraction of these emotions from images of human faces can help in human–computer interaction, among other things. Convolution Neural Network (CNN), Deep Belief Network (DBN), Bi-directional Long Short Term Memory (Bi-LSTM) are some of the existing techniques used to recognize the emotions of a human. This technique has some impacts like low accuracy and high error. To achieve better accuracy, hybrid CNN-SVM (Support Vector Machine) model is designed for classifying emotional state of humans. Initially, preprocessing is used to remove unwanted things from the image dataset. Resizing, Gaussian filter, Median filter …