Abstract
Human beings are particularly inclined to express real emotions through micro-expressions with subtle amplitude and short duration. Though people regularly recognize many distinct emotions, for the most part, research studies have been limited to six basic categories: happiness, surprise, sadness, anger, fear, and disgust. Like normal expressions (i.e., macro-expressions), most current research into micro-expression recognition focuses on these six basic emotions. This paper describes an important group of micro-expressions, which we call compound emotion categories. Compound micro-expressions are constructed by combining two basic micro-expressions but reflect more complex mental states and more abundant human facial emotions. In this study, we firstly synthesized a Compound Micro-expression Database (CMED) based on existing spontaneous micro-expression datasets. These subtle feature of micro-expression makes it difficult to observe its motion track and characteristics. Consequently, there are many challenges and limitations to synthetic compound micro-expression images. The proposed method firstly implemented Eulerian Video Magnification (EVM) method to enhance facial motion features of basic micro-expressions for generating compound images. The consistent and differential facial muscle articulations (typically referred to as action units) associated with each emotion category have been labeled to become the foundation of generating compound micro-expression. Secondly, we extracted the apex frames of CMED by 3D Fast Fourier Transform (3D-FFT). Moreover, the proposed method calculated the optical flow information between the onset frame and apex frame to produce an optical flow feature map. Finally, we designed a shallow network to extract high-level features of these optical flow maps. In this study, we synthesized four existing databases of spontaneous micro-expressions (CASME I, CASME II, CAS(ME)2, SAMM) to generate the CMED and test the validity of our network. Therefore, the deep network framework designed in this study can well recognize the emotional information of basic micro-expressions and compound micro-expressions.
Highlights
A micro-expression (ME) is an involuntary facial movement lasting less than one fifteenth of a second which can betray “hidden” or secretive emotions that the expresser attempts to conceal [1]
Psychologists have posed countless questions about facial expressions as they relate to human emotions: How many kinds of expressions can people make? Does every facial expression represent emotions: How many kinds of expressions can people make? Does every facial expression represent a sign of emotion directly? Can a person lie using their expressions to lying verbally? In a sign of emotion directly? Can a person lie using their expressions to lying verbally? In approaching these questions, researchers have given us a deeper understanding of human emotional approaching these questions, researchers have given us a deeper understanding of human emotional states [10,11]
We only input the enlarged images of MEs into Convolutional Neural Network (CNN), and the results show that α = 20 is the appropriate magnification
Summary
A micro-expression (ME) is an involuntary facial movement lasting less than one fifteenth of a second which can betray “hidden” or secretive emotions that the expresser attempts to conceal [1]. ME analysis has become an effective means to detect deception, and has many potential applications in the security field. Unlike macro-expressions, physiological studies have shown that MEs occur over a remarkably brief duration with only minor muscle changes, which makes them very challenging to detect and recognize [2]. Most facial micro-expressions research has focused on the study of six emotions typically seen in most cultures: happiness, surprise, anger, sadness, fear, and disgust [3,4,5,6].
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