Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition

Cross-Database Micro-Expression Recognition (CDMER) aims to develop the Micro-Expression Recognition (MER) methods with strong domain adaptability, i.e., the ability to recognize the Micro-Expressions (MEs) of different subjects captured by different imaging devices in different scenes. The development of CDMER is faced with two key problems: 1) the severe feature distribution gap between the source and target databases; 2) the feature representation bottleneck of ME such local and subtle facial expressions. To solve these problems, this paper proposes a novel Transfer Group Sparse Regression method, namely TGSR, which aims to 1) optimize the measurement and better alleviate the difference between the source and target databases, and 2) highlight the valid facial regions to enhance extracted features, by the operation of selecting the group features from the raw face feature, where each region is associated with a group of raw face feature, i.e., the salient facial region selection. Compared with previous transfer group sparse methods, our proposed TGSR has the ability to select the salient facial regions, which is effective in alleviating the aforementioned problems for better performance and reducing the computational cost at the same time. We use two public ME databases, i.e., CASME II and SMIC, to evaluate our proposed TGSR method. Experimental results show that our proposed TGSR learns the discriminative and explicable regions, and outperforms most state-of-the-art subspace-learning-based domain-adaptive methods for CDMER

A review and framework for designing interactive technologies for emotion regulation training

Emotion regulation is foundational to mental health and well-being. In the last ten years there has been an increasing focus on the use of interactive technologies to support emotion regulation training in a variety of contexts. However, research has been done in diverse fields, and no cohesive framework exists that explicates what features of such system are important to consider, guidance on how to design these features, and what remains unknown, which should be explored in future design research. To address this gap, this thesis presents the results of a descriptive review of 54 peer-reviewed papers. Through qualitative and frequency analysis I analyzed previous technologies, reviewed their theoretical foundations, the opportunities where they appear to provide unique benefits, and their conceptual and usability challenges. Based on the findings I synthesized a design framework that presents the main concepts and design considerations that researchers and designers may find useful in designing future technologies in the context of emotion regulation training