Macro- and micro-expression spotting in long videos using spatio-temporal strain

Shreve, Matthew; Godavarthy, Sridhar; Goldgof, Dmitry B.; Sarkar, Sudeep

doi:10.1109/fg.2011.5771451

Cited by 160 publications

(116 citation statements)

References 8 publications

Supporting

Mentioning

109

Contrasting

Order By: Relevance

“…They were formally named microexpressions by Ekman (Ekman & Friesen, 1969) and presently there is great interest in this subject in research projects related to lying and deception detection (Ekman, 2009;Matsumoto & Hwang, 2011;Porter & ten Brinke, 2008, 2010Vrij et al, 2010). There are also works using computer vision techniques and learning algorithms to automatically recognize microexpressions in video sequences (Metaxas & Zhang, 2013;Michael, Dilsizian, Metaxas, & Burgoon, 2010;Pfister, Li, Zhao, & Pietikainen, 2011;Shreve, Godavarthy, Goldgof, & Sarkar, 2011;Wu, Shen, & Fu, 2011). Recently databases have been created (Yan, Wu, Liu, Wang, & Fu, 2013;X.…”

Section: Related Workmentioning

confidence: 99%

Investigating Macroexpressions and Microexpressions in Computer Graphics Animated Faces

Queiroz

Musse

Badler

2014

Presence: Teleoperators and Virtual Environments

View full text Add to dashboard Cite

Due to varied personal, social, or even cultural situations, people sometimes conceal or mask their true emotions. These suppressed emotions can be expressed in a very subtle way by brief movements called microexpressions. We investigate human subjects' perception of hidden emotions in virtual faces, inspired by recent psychological experiments. We created animations with virtual faces showing some facial expressions and inserted brief secondary expressions in some sequences, in order to try to convey a subtle second emotion in the character. Our evaluation methodology consists of two sets of experiments, with three different sets of questions. The first experiment verifies that the accuracy and concordance of the participant's responses with synthetic faces matches the empirical results done with photos of real people in the paper by X.-b. Shen, Q. Wu, and X.-l. Fu, 2012, "Effects of the duration of expressions on the recognition of microexpressions," Journal of Zhejiang University Science B, 13(3), 221-230. The second experiment verifies whether participants could perceive and identify primary and secondary emotions in virtual faces. The third experiment tries to evaluate the participant's perception of realism, deceit, and valence of the emotions. Our results show that most of the participants recognized the foreground (macro) emotion and most of the time they perceived the presence of the second (micro) emotion in the animations, although they did not identify it correctly in some samples. This experiment exposes the benefits of conveying microexpressions in computer graphics characters, as they may visually enhance a character's emotional depth through subliminal microexpression cues, and consequently increase the perceived social complexity and believability (Shen, Wu, & Fu, 2012). The second experiment verifies if participants could perceive and identify primary and secondary emotions in virtual faces. The third tries to evaluate the participant's perception of realism, deceit and valence of the emotions. Our results show that most of the participants recognized the foreground (macro) emotion and most of the time they had perceived the presence of the second (micro) emotion in the animations, although they did not identify it correctly in some samples. This experiment exposes the benefits of conveying microexpressions in Computer Graphics characters, as they may visually enhance a character's emotional "depth" through subliminal microexpression cues and consequently increase its perceived social complexity and believability.

show abstract

Section: Related Workmentioning

confidence: 99%

Investigating Macroexpressions and Microexpressions in Computer Graphics Animated Faces

Queiroz

Musse

Badler

2014

Presence: Teleoperators and Virtual Environments

View full text Add to dashboard Cite

show abstract

“…Shreve et al [41,42] computed the strain magnitude of optical flow to discriminate micro-expressions from macro-expressions by observing the interval flow in a given threshold. They evaluated their methods on BU [52], USF and USF-HD databases.…”

Section: Micro-expression Recognitionmentioning

confidence: 99%

Effective recognition of facial micro-expressions with video motion magnification

Wang

See

et al. 2016

Multimed Tools Appl

View full text Add to dashboard Cite

“…In [85], micro-expressions were further classified into positive (happiness) and negative (disgust, fear, surprise and sadness) ones. In [86], both macro-and microexpressions in video sequences were detected. However, to the best knowledge of the author, no computer vision research has been conducted to study the difference between macro-and micro-expressions as indicators of deception.…”

Section: Micro-expression Vs Macro-expression As a Facial Clue To Dementioning

confidence: 99%

Does “lie to me” lie to you? An evaluation of facial clues to high-stakes deception

Lin

Levine

2016

Computer Vision and Image Understanding

View full text Add to dashboard Cite

During a forensic interview, high-stakes deception is very prevalent notwithstanding the heavy consequences that may result. Studies have shown that most untrained people cannot perform well in discerning liars and truth-tellers.Thus it has become common to adopt various technical aids to compensate for this poor judgment. Examples are polygraphs, functional Magnetic Resonance Imaging (fMRI) and linguistic analysis. However, the deception indicators used in these cases are not reliable.In the popular TV program Lie to Me, micro-expressions have been used for detecting deceit during the investigation of some criminal cases. A microexpression is considered to be a rapid and involuntary facial expression which could reveal the concealed emotion. Additionally, some psychological studies have stated that certain facial actions are more difficult to inhibit if the associated facial expressions are genuine. Similarly, these facial expressions are equally difficult to fake. This has cast light on the possibility that deception could be detected by analyzing these facial actions. However, to the best knowledge of the author, there is no computer vision research that has attempted to discriminate high-stakes deception from truth using facial expressions. Therefore, this thesis aims to test the validity of facial clues to deception detection in high-stakes situations using computer vision approaches.We note that only a limited number of the existing databases have been collected specifically for deception detection studies and none of them were obtained from real-world situations. In this thesis we present a video database of actual highstakes situations, which we have created using YouTube.We have adopted 2D appearance-based methods as the methodology to characterize the 3D facial features. Instead of building a 3D head model as is the current trend, we have extracted invariant 2D features that are related to the 3D characteristic.iii In order to discern deception and honesty, we have identified the following deceptive cues from nine separate facial regions through dynamic facial analysis: eye blink, eyebrow motion, wrinkle occurrence and mouth motion. Then these cues were integrated to form a facial behavior pattern vector. A Random Forest was trained using the collected database and applied to classify the facial patterns into deceptive and truthful categories.Despite the many uncontrolled factors (illumination, head pose and facial occlusion) in the videos in our database, we have achieved an accuracy of 76.92% when discriminating liars from truth-tellers using both micro-expressions and

show abstract

Macro- and micro-expression spotting in long videos using spatio-temporal strain

Cited by 160 publications

References 8 publications

Investigating Macroexpressions and Microexpressions in Computer Graphics Animated Faces

Investigating Macroexpressions and Microexpressions in Computer Graphics Animated Faces

Effective recognition of facial micro-expressions with video motion magnification

Does “lie to me” lie to you? An evaluation of facial clues to high-stakes deception

Contact Info

Product

Resources

About