SAMM: A Spontaneous Micro-Facial Movement Dataset

• Published in: IEEE transactions on affective computing Vol. 9; no. 1; pp. 116 - 129
• Main Authors: Davison, Adrian K., Lansley, Cliff, Costen, Nicholas, Tan, Kevin, Moi Hoon Yap
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; baseline; Datasets; Encoding; Face recognition; facial action coding system; facial analysis; Feature extraction; Lighting; Machine learning; micro-expressions; Micro-movements; Motion perception; Performance enhancement; Reliability; State of the art; Training
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2016.2573832

Micro-facial expressions are spontaneous, involuntary movements of the face when a person experiences an emotion but attempts to hide their facial expression, most likely in a high-stakes environment. Recently, research in this field has grown in popularity, however publicly available datasets of micro-expressions have limitations due to the difficulty of naturally inducing spontaneous micro-expressions. Other issues include lighting, low resolution and low participant diversity. We present a newly developed spontaneous micro-facial movement dataset with diverse participants and coded using the Facial Action Coding System. The experimental protocol addresses the limitations of previous datasets, including eliciting emotional responses from stimuli tailored to each participant. Dataset evaluation was completed by running preliminary experiments to classify micro-movements from non-movements. Results were obtained using a selection of spatio-temporal descriptors and machine learning. We further evaluate the dataset on emerging methods of feature difference analysis and propose an Adaptive Baseline Threshold that uses individualised neutral expression to improve the performance of micro-movement detection. In contrast to machine learning approaches, we outperform the state of the art with a recall of 0.91. The outcomes show the dataset can become a new standard for micro-movement data, with future work expanding on data representation and analysis.