Face-Detection-and-Emotion-Recognition. In this project, we take into account different approaches like Eigenfaces, Principal Component Analysis(PCA), Support Vector Machines(SVM), Artificial Neural Networks(ANN), Convolutional Neural Networks(CNN), K-Nearest Neighbour(KNN) for the problem of face recognition and compare all the approaches on the basis of different performance metrics such as Accuracy, Number of Iterations and Error Rate to see which technique is more feasible in real life. Then after we also recognize emotions in a face using the Support Vector Machines(SVM) and the Convolutional Neural Networks(CNN). The approaches we have considered treats Face Recognition and Emotions Recognition problem as two-dimensional recognition problem, the advantage being faces can be described by a small set of 2-D characteristics views. The dataset used is collected from the whole class where each student was asked to upload their selfies in six different emotions.