Emotion recognition plays a critical role in various domains, such as human-computer interaction, psychology, and market research. With the growing popularity of social media platforms and the increasing use of multimedia content, the ability to automatically recognize emotions from images has become a significant research area. In this research, through a pre-trained CNN model, rich and high-level features that effectively capture the emotional content present in the images are extracted. The dataset for the same is FER2013 which is downloaded through Kaggle and the data to test emotion will be taken live feed through a webcam. The extracted features are then fed into three classifiers namely: CNN, KNN, and random forest. Conducted experiments on a publicly available emotion recognition dataset named FER-2013. It illustrates the efficacy of change in accuracy by achieving a vast difference between the CNN, KNN, and Random Forest. Moreover, compared the performance with other state-of-the-art methods, demonstrating its superior performance in accurately recognizing emotions from images.