Emotion recognition by the human brain, normally incorporates context, body language, facial expressions, verbal cues, non-verbal cues, gestures and tone of voice. When considering only the face, piecing together various aspects of each facial feature is critical in identifying the emotion. Since viewing a single facial feature in isolation may result in inaccuracies, this paper attempts training neural networks to first identify specific
facial features in isolation, and then use the general pattern of expressions on the face to identify the overall emotion. The reason for classification inaccuracies are also examined.