Image Processing Pipeline for Facial Expression Recognition under Variable Lighting 1 Introduction Automated facial expression recognition has proven to be beneficial in a variety of settings. For instance, in the Wall Lab of Stanford Medical School, expression recognition is used in a Google Glass application that helps children and adults with Autism detect the emotions of people they are interacting with. Thus, research into increased classification accuracy for expression recognition can have great impact. Many studies addressing this subject use images with uniform lighting conditions[1]. This is understandable because it allows for accurate evaluation of the recognition algorithm. However, for most practical applications, the emotions recognition task is done in real-world conditions where the lighting is diverse and far from being uniform[2]. In this project, we aim to study the effects of different lighting/shadowing on the emotions recognition task and find t...