Abstract-Automatic Facial Expression Recognition and Analysis, in particular FACS Action Unit (AU) detection and discrete emotion detection, has been an active topic in computer science for over two decades. Standardisation and comparability has come some way; for instance, there exist a number of commonly used facial expression databases. However, lack of a common evaluation protocol and lack of sufficient details to reproduce the reported individual results make it difficult to compare systems to each other. This in turn hinders the progress of the field. A periodical challenge in Facial Expression Recognition and Analysis would allow this comparison in a fair manner. It would clarify how far the field has come, and would allow us to identify new goals, challenges and targets. In this paper we present the first challenge in automatic recognition of facial expressions to be held during the IEEE conference on Face and Gesture Recognition 2011, in Santa Barbara, California. Two sub-challenges are defined: one on AU detection and another on discrete emotion detection. It outlines the evaluation protocol, the data used, and the results of a baseline method for the two sub-challenges.

Abstract-As one of the most comprehensive and objective ways to describe facial expressions, the Facial Action Coding System (FACS) has recently received significant attention. Over the past 30 years, extensive research has been conducted by psychologists and neuroscientists on various aspects of facial expression analysis using FACS. Automating FACS coding would make this research faster and more widely applicable, opening up new avenues to understanding how we communicate through facial expressions. Such an automated process can also potentially increase the reliability, precision and temporal resolution of coding. This paper provides a comprehensive survey of research into machine analysis of facial actions. We systematically review all components of such systems: pre-processing, feature extraction and machine coding of facial actions. In addition, the existing FACS-coded facial expression databases are summarised. Finally, challenges that have to be addressed to make automatic facial action analysis applicable in real-life situations are extensively discussed. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the future of machine recognition of facial actions: what are the challenges and opportunities that researchers in the field face.

Recently developed appearance descriptors offer the opportunity for efficient and robust facial expression recognition. In this paper we investigate the merits of the family of local binary pattern descriptors for FACS Action-Unit (AU) detection. We compare Local Binary Patterns (LBP) and Local Phase Quantisation (LPQ) for static AU analysis. To encode facial expression dynamics, we extend the purely spatial representation LPQ to a dynamic texture descriptor which we call Local Phase Quantisation from Three Orthogonal Planes (LPQ-TOP), and compare this with the Local Binary Patterns from Three Orthogonal Planes (LBP-TOP). The efficiency of these descriptors is evaluated by a fully automatic AU detection system and tested on posed and spontaneous expression data collected from the MMI and SEMAINE databases. Results show that the systems based on LPQ achieve higher accuracy rate than those using LBP, and that the systems that utilise dynamic appearance descriptors outperform those that use static appearance descriptors. Overall, our proposed LPQ-TOP method outperformed all other tested methods.

Abstract-Automatic facial expression recognition has been an active topic in computer science for over two decades, in particular facial action coding system action unit (AU) detection and classification of a number of discrete emotion states from facial expressive imagery. Standardization and comparability have received some attention; for instance, there exist a number of commonly used facial expression databases. However, 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 meta-analysis 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 subchallenges: AU detection and classification of facial expression imagery in terms of a number of discrete emotion categories. We also summarize the lessons learned and reflect on the future of the field of facial expression recognition in general and on possible future challenges in particular.

Abstract-Both the configuration and the dynamics of facial expressions are crucial for the interpretation of human facial behaviour. Yet to date, the vast majority of reported efforts in the field either do not take the dynamics of facial expressions into account or focus only on prototypic facial expressions of six basic emotions. Facial dynamics can be explicitly analysed by detecting the constituent temporal segments of Facial Action Coding System's (FACS) Action Units (AUs) -onset, apex, and offset. In this work, we present a novel approach to explicit analysis of temporal dynamics of facial actions using the dynamic appearance descriptor Local Phase Quantisation from Three Orthogonal Planes (LPQ-TOP). Temporal segments are detected by combining a discriminative classifier for detecting the temporal segments on a frame-by-frame basis with Markov Models that enforce temporal consistency over the whole episode. The system is evaluated in detail over the MMI facial expression database, the UNBC-McMaster pain database, the SAL database and the GEMEP-FERA dataset in database-dependent experiments, and in cross-database experiments using the CohnKanade and the SEMAINE databases. The comparison with other state-of-the-art methods shows that the proposed LPQ-TOP method outperforms other approaches for the problem of AU temporal segment detection, and that overall AU activation detection benefits from dynamic appearance information.