We present a system for covert automated deception detection in real-life courtroom trial videos. We study the importance of different modalities like vision, audio and text for this task. On the vision side, our system uses classifiers trained on low level video features which predict human micro-expressions. We show that predictions of high-level micro-expressions can be used as features for deception prediction. Surprisingly, IDT (Improved Dense Trajectory) features which have been widely used for action recognition, are also very good at predicting deception in videos. We fuse the score of classifiers trained on IDT features and high-level micro-expressions to improve performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio domain also provide a significant boost in performance, while information from transcripts is not very beneficial for our system. Using various classifiers, our automated system obtains an AUC of 0.877 (10-fold cross-validation) when evaluated on subjects which were not part of the training set. Even though state-of-the-art methods use human annotations of micro-expressions for deception detection, our fully automated approach outperforms them by 5 . When combined with human annotations of micro-expressions, our AUC improves to 0.922. We also present results of a user-study to analyze how well do average humans perform on this task, what modalities they use for deception detection and how they perform if only one modality is accessible. Our project page can be found at this https URL .
Deceptive behavior is very subtle and varies across different people. Thus, detecting these subtle micro motion patterns, e.g. micro facial expression, itself is a challenging problem. In addition, @cite_24 suggested that research should focus more on the movement dynamics and behavior structure. Motivated by this, we directly describe behavioral dynamics without detecting facial landmarks, then use behavior dynamics to learn micro-expressions and deceptive behavior. We also include simple verbal and audio features, as other multi-modal approaches @cite_6 @cite_9 , into the overall covert automated system.
@cite_24 @cite_9 @cite_6
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One of important cues of deception detection is micro-expression. It has three characteristics: short duration, low intensity and usually local movements. These characteristics imply that micro-expression is sparse. In this paper, we use the sparse part of Robust PCA (RPCA) to extract the subtle motion information of micro-expression. The local texture features of the information are extracted by Local Spatiotemporal Directional Features (LSTD). In order to extract more effective local features, 16 Regions of Interest (ROIs) are assigned based on the Facial Action Coding System (FACS). The experimental results on two micro-expression databases show the proposed method gain better performance. Moreover, the proposed method may further be used to extract other subtle motion information (such as lip-reading, the human pulse, and micro-gesture etc.) from video. Deception is a very common phenomenon and its detection can be beneficial to our daily lives. Compared with other deception cues, micro-expression has shown great potential as a promising cue for deception detection. The spotting and recognition of micro-expression from long videos may significantly aid both law enforcement officers and researchers. However, database that contains both micro-expression and macro-expression in long videos is still not publicly available. To facilitate development in this field, we present a new database, Chinese Academy of Sciences Macro-Expressions and Micro-Expressions (CAS(ME) @math ), which provides both macro-expressions and micro-expressions in two parts (A and B). Part A contains 87 long videos that contain spontaneous macro-expressions and micro-expressions. Part B includes 300 cropped spontaneous macro-expression samples and 57 micro-expression samples. The emotion labels are based on a combination of action units (AUs), self-reported emotion for every facial movement, and the emotion types of emotion-evoking videos. Local Binary Pattern (LBP) was employed for the spotting and recognition of macro-expressions and micro-expressions and the results were reported as a baseline evaluation. The CAS(ME) @math database offers both long videos and cropped expression samples, which may aid researchers in developing efficient algorithms for the spotting and recognition of macro-expressions and micro-expressions. Summary In highly social species such as humans, faces have evolved to convey rich information for social interaction, including expressions of emotions and pain [1–3]. Two motor pathways control facial movement [4–7]: a subcortical extrapyramidal motor system drives spontaneous facial expressions of felt emotions, and a cortical pyramidal motor system controls voluntary facial expressions. The pyramidal system enables humans to simulate facial expressions of emotions not actually experienced. Their simulation is so successful that they can deceive most observers [8–11]. However, machine vision may be able to distinguish deceptive facial signals from genuine facial signals by identifying the subtle differences between pyramidally and extrapyramidally driven movements. Here, we show that human observers could not discriminate real expressions of pain from faked expressions of pain better than chance, and after training human observers, we improved accuracy to a modest 55 . However, a computer vision system that automatically measures facial movements and performs pattern recognition on those movements attained 85 accuracy. The machine system's superiority is attributable to its ability to differentiate the dynamics of genuine expressions from faked expressions. Thus, by revealing the dynamics of facial action through machine vision systems, our approach has the potential to elucidate behavioral fingerprints of neural control systems involved in emotional signaling. Spontaneous facial micro-expression analysis has become an active task for recognizing suppressed and involuntary facial expressions shown on the face of humans. Recently, Local Binary Pattern from Three Orthogonal Planes (LBP-TOP) has been employed for micro-expression analysis. However, LBP-TOP suffers from two critical problems, causing a decrease in the performance of micro-expression analysis. It generally extracts appearance and motion features from the sign-based difference between two pixels but not yet considers other useful information. As well, LBP-TOP commonly uses classical pattern types which may be not optimal for local structure in some applications. This paper proposes SpatioTemporal Completed Local Quantization Patterns (STCLQP) for facial micro-expression analysis. Firstly, STCLQP extracts three interesting information containing sign, magnitude and orientation components. Secondly, an efficient vector quantization and codebook selection are developed for each component in appearance and temporal domains to learn compact and discriminative codebooks for generalizing classical pattern types. Finally, based on discriminative codebooks, spatiotemporal features of sign, magnitude and orientation components are extracted and fused. Experiments are conducted on three publicly available facial micro-expression databases. Some interesting findings about the neighboring patterns and the component analysis are concluded. Comparing with the state of the art, experimental results demonstrate that STCLQP achieves a substantial improvement for analyzing facial micro-expressions. HighlightsWe propose spatiotemporal completed local quantized pattern for micro-expression analysis.We propose to use three useful information, including the sign-based, magnitude-based and orientation-based difference of pixels for LBP.We propose to use an efficient vector quantization and discriminative codebook selection to make LBP-TOP more discriminative and compact.We evaluate the framework on three publicly available facial micro-expression databases.We evaluate the influence of parameters, different components and codebook selection to spatiotemporal completed local quantized pattern.