The affective quality called Valence refers to the intrinsic goodness (positive valence) or badness (negative valence) of an event, object, or situation. For this purpose, a model for classification and characterization of emotions have been developed which is discussed in this paper. In this model, the images are smoothened using an Average Filter and are first identified through a Convolutional Neural Network which uses the ReLU activation function. Then, the valence is classified using a Support Vector Machine (SVC) classifier, which uses a Radial Basis Function (RBF) kernel. For this reason, the emotions are labeled according to their nature. The positive emotions are labeled 1 (inclusive of the neutral emotion) and the negative emotions are labeled as 0. The images from the FER 2013 dataset is used for Valence Recognition and is given via a RBF Kernel in a SVM, which classifies whether the emotion recognized is positive or negative. The haarcascade algorithm is implemented to detect the face. In this paper, the 7 human emotions (happiness, surprise, fear, anger, fear, disgust, sadness and neutral) have been identified and their valence recognized.