Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/560 2018 2018 Bharati Vidyapeeth's College of Engineering, INDIA Shubham Gupta Bharati Vidyapeeth's College of Engineering, INDIA Aarushi Dhawan Bharati Vidyapeeth's College of Engineering, INDIA Arpit Gupta Bharati Vidyapeeth's College of Engineering, INDIA A. Kumar Dubey Human facial emotion recognition has attracted interest in the field of Artificial Intelligence. The emotions on a human face depict what's going on inside the mind. Facial expression recognition is the part of Facial recognition which is gaining more importance, and the need for it has increased tremendously. Though there are methods to identify expressions using machine learning and Artificial Intelligence techniques, this work attempts to use convolution neural networks to recognize expressions and classify the expressions into 6 emotional categories. Various datasets are investigated and explored for training expression recognition models are explained in this paper, and the models which are used in this paper are VGG 19 and RESNET 18. We included facial emotional recognition with gender identification also. In this project, we have used the fer2013 and ck+ datasets and ultimately achieved 73% and 94% around accuracies, respectively. 2020 2020 57 63 10.54216/FPA.020203 https://www.americaspg.com/articleinfo/3/show/560