Abstract

Automatic analysis of facial gestures is rapidly becoming an area of intense interest in computer science and human-computer interaction design communities. However, the basic goal of this area of research – translating detected facial changes into a human-like description of shown facial expression – is yet to be achieved. One of the main impediments to achieving this aim is the fact that human interpretations of a facial expression differ depending upon whether the observed person is speaking or not. A first step in tackling this problem is to achieve automatic detection of facial gestures that are typical for speech articulation. This paper presents our approach to seizing this step in the research on automatic facial expression analysis. It presents all: a robust and flexible method for recognition of 22 facial muscle actions from face image sequences, a method for automatic determination of whether the observed subject is speaking or not, and an experimental study on facial muscle actions typical for speech articulation.