Face detection is the foremost task in building vision-based human-computer interaction systems and in particular in applications such as face recognition, face identification, face tracking, expression recognition and content based image retrieval. A robust face detection system must be able to detect faces irrespective of illuminations, shadows, cluttered backgrounds, facial pose, orientation and facial expressions. Many approaches for face detection have been proposed. However, as revealed by FRVT 2002 tests, face detection in outdoor images with uncontrolled illumination and in images with varied pose (non-frontal profile views) is still a serious problem. In this article, we describe a Local-Global Graph (LGG) based method for detecting faces and for recognizing facial expressions in real world image capturing conditions both indoor and outdoor, and with a variety of illuminations (shadows, high-lights, non-white lights) and in cluttered backgrounds. First, a neural color constancy based skin detection procedure to detect skin in complex real world images is presented. Second, the LGG method for detecting faces and facial expressions with a maximum confidence from skin segmented images is presented. The LG Graph embeds both the local information (the shape of facial feature is stored within the local graph at each node) and the global information (the topology of the face). The LGG approach presented here emulates the human visual perception for face detection. In general, humans first extract the most important facial features such as eyes, nose, mouth, etc. and then inter-relate them for face and facial expression representations. Facial expression recognition from the detected face images is obtained by comparing the LG Expression Graphs with the existing expression models present in the LGG database. The methodology is accurate for the expression models present in the database. [ABSTRACT FROM AUTHOR]