Meta-Analyis of the First Facial Expression Recognition Challenge

Automatic Facial Expression Recognition has been an active topic in computer science for over two decades, in particular Facial Action Coding System (FACS) Action Unit (AU) detection and classification of a number of discrete emotion states from facial expressive imagery. Standardisation and comparability has received some attention; for instance, there exist a number of commonly used facial expression databases. But lack of a commonly accepted evaluation protocol and typically lack of sufficient details needed to reproduce the reported individual results make it difficult to compare systems. This in turn hinders the progress of the field. A periodical challenge in Facial Expression Recognition would allow such a comparison on a level playing field. It would provide an insight on how far the field has come, and would allow researchers to identify new goals, challenges and targets. This paper presents a metaanalysis of the first such challenge in automatic recognition of facial expressions, held during the IEEE conference on Face and Gesture Recognition 2011. It details the challenge data, evaluation protocol, and the results attained in two sub-challenges: AU detection and classification of facial expression imagery in terms of a number of discrete emotion categories. We also summarise the lessons learned and reflect on the future of the field of facial expression recognition in general and on possible future challenges in particular.