Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems.
Advances in computer algorithms and video acquisition technology have rendered machine analysis of facial micro-expressions possible today, in contrast to decades ago when it was primarily the domain of psychiatrists where analysis was Automatic facial expression analysis survey manual.
Indeed, although the study of facial micro-expressions is a well-established field in psychology, it is still relatively new from the computational perspective with many interesting problems.
In this survey, we present a comprehensive review of state-of-the-art databases and methods for micro-expressions spotting and recognition.
Individual stages involved in the automation these tasks are also described and reviewed at length. In addition, we also deliberate on the challenges and future directions in this growing field of automatic facial micro-expression analysis.
InEkman and Friesen spotted a quick full-face emotional expression in a filmed interview which revealed a strong negative feeling a Automatic facial expression analysis survey patient was trying to Automatic facial expression analysis survey from her psychiatrist in order to convince that she was no longer suicidal. This type of facial expressions is called micro-expressions MEs and they were actually first discovered by Haggard and Isaacs 3 years before the event happened.
In their study, Haggard and Isaacs discovered these micromomentary expressions while scanning motion picture films of psychotherapy hours, searching for indications of non-verbal communication between patient and Automatic facial expression analysis survey. MEs are very brief, subtle, and involuntary facial expressions which normally occur when a person either deliberately or unconsciously conceals his or her genuine emotions Ekman and Friesen, ; Ekman, b.
Recent research by Yan et al. Besides short duration, MEs also have other significant characteristics such as low intensity and fragmental facial action units where only part of the action units of full-stretched facial expressions are presented Porter and Ten Brinke,
Automatic facial expression analysis survey Yan et al. Due to these three characteristics of the MEs, it is difficult for human beings to perceive micro-expressions with the naked eye. In spite of these challenges, new psychological studies of MEs and computational methods to spot and recognize MEs have been gaining more attention lately because of its potential applications in many fields, i.
However, it was found in Frank et al. Thus, an automatic ME recognition system is in great need in order to help detect MEs such as those exhibited in lies and
Automatic facial expression analysis survey behaviors, especially with the modern advancements in computational power and parallel multi-core functionalities.
These have enabled researchers to perform video processing operations that used to be infeasible decades ago, increasing the capability of computer-based understanding of "Automatic facial expression analysis survey" in solving different real-life vision problems.
Correspondingly, in recent years researchers have moved beyond psychology to using computer vision and video processing techniques to automate the task of recognizing MEs. Although normal facial expression recognition is now considered a well-established and popular research topic with many good algorithms developed Zeng et al. One of the challenges faced by this field is spotting the ME of a person accurately from a video sequence.
As a ME is subtle and short, spotting of MEs is not an easy task. Furthermore, spotting of MEs becomes harder if the video clip consists of spontaneous facial expressions and unrelated facial movements, i. On the other hand, other challenges of ME recognition include inadequate features for recognizing MEs due to its low change in intensity and lack of complete, spontaneous and dynamic ME databases. In the past few years, there have been some noteworthy advances in the field of automatic ME spotting and recognition.
However, there is currently no comprehensive review to chart the emergence of this field and summarize the development of techniques introduced to solve these tasks.
In this survey paper, we first discuss the existing ME corpora. ME spotting focuses on finding the occurrence of MEs "Automatic facial expression analysis survey" a video sequence while ME recognition involves assigning an emotion class label to an ME sequence.
For both tasks, we look into the range of methods that have Automatic facial expression analysis survey proposed and applied to various stages of these tasks. Lastly, we discuss the challenges in ME recognition and suggest some potential future directions.
The prerequisite of developing any automatic ME recognition system is having enough labeled affective data. As ME research in computer vision has only gained attention in the past few years, the number of publicly available spontaneous ME databases is still relatively low. The key difference between posed and spontaneous MEs Automatic facial expression analysis survey in the relevance between expressed facial movement and underlying emotional state.
For posed MEs, facial expressions are deliberately shown and irrelevant to the present emotion of senders, therefore not really helpful for the recognition of real subtle emotions. Meanwhile, spontaneous MEs are the unmodulated facial expressions that are congruent with an underlying emotional state Hess and Kleck, Due to the nature of the posed and spontaneous MEs, the techniques for inducing facial expressions for purpose of constructing a database are contrasting.
For the case of posed MEs, subjects are usually asked to relive an emotional experience or even watching example videos containing MEs prior to the recording session and perform the expression as well as possible. However, eliciting spontaneous MEs is more challenging as the subjects have to be involved emotionally.
Usually, emotionally evocative video episodes are used to induce the genuine emotional state of subjects, and the subjects have to attempt to suppress their true emotions or risk getting penalized.
According to Ekman and Friesen and Ekman aMEs are involuntary which could not be created intentionally.
Thus, posed MEs usually do not exhibit the characteristics i. In addition, the occurrence duration of their micro-expressions i. To have a more ecological validity, research interest then shifted to spontaneous ME databases.
Several groups have developed a few spontaneous MEs databases to aid researchers in the development of automatic ME spotting and recognition algorithms.
To elicit MEs spontaneously, participants "Automatic facial expression analysis survey" induced by watching emotional video clips to experience a high arousal, aided by an incentive or penalty to motivate the disguise of emotions. However, due to the challenging process of eliciting these spontaneous MEs, the number of samples collected for these ME databases is still limited.
The Silesian Deception database Radlak et al. This dataset is not commonly used in spotting and recognition literature as it does not involve expressions per se ; its inception primarily for the purpose of automatic deception recognition.
Images reproduced from the database with permission from Li et al. However, some videos are extremely short, i. Images reproduced from the database Automatic facial expression analysis survey permission from Yan et al.
In SMIC-E databases, long video clips that contain some additional non-micro frames before and after the labeled micro frames were included as well. Part A contains both spontaneous macro-expressions and MEs in long videos; and Part B includes cropped expression samples with frame from onset to offset.
However, CAS ME 2 is recorded using a low frame-rate 25 fps camera due to the need to capture both macro- and micro-expressions. Emotion classes are then labeled by trained experts later. In addition, about neutral frames Automatic facial expression analysis survey included before and after the occurrence of the micro-movement, which makes spotting feasible.
The SAMM is arguably the most culturally diverse database among all of them. Images reproduced from the database with permission from Davison et al. Poker games are highly competitive with players often try to conceal or fake their true emotions, which facilitates likely occurrences of MEs. With the camera view switching often, the entire shot with a single face in video averaging 3s in duration was taken.
A total of 31 videos with 16 individuals were collected. Images reproduced from the database Husak et al. Automatic ME analysis involves two tasks: ME spotting and ME recognition. Facial ME spotting refers to the problem of automatically detecting the temporal interval of a micro-movement in a sequence of video frames; and ME recognition is the classification task to identify the ME involved in the video samples. In a complete facial ME recognition system, accurately and precisely identifying frames containing facial micro-movements which contribute to facial MEs in a video is a prerequisite for high-level facial analysis i.
Thus, the automatic facial expression spotting frameworks are developed to automatically search the temporal dynamics of MEs in streaming videos. Temporal dynamics refer to the motions of facial MEs that involve onset startapex peakoffset endand neutral phases.
According to the work by Valstar and Panticthe onset phase is the moment where muscles are contracting and appearance Automatic facial expression analysis survey facial changes grows stronger; the apex phase is the moment where the expression peaks the most obvious ; and the offset phase is the instance where the muscles are relaxing and the face to its neutral Automatic facial expression analysis survey little or no activation of facial muscles.
Typically a facial motion shifts through the sequence of neutral-onset-apex-offset-neutral, but other combinations such as multiple apices are also possible. A video sequence depicting the order in which onset, apex and offset frames occur. In general, a facial ME spotting framework consists of a few stages: The details of each of the stages will be further discussed in the following sections. In facial ME spotting, the general
Automatic facial expression analysis survey steps include facial landmark detection, facial landmark tracking, face registration, face masking, and face region retrieval.
Facial landmark detection is the first most important step in the spotting framework to locate the facial points on the facial images. In the field of MEs, two ways of locating the facial points are applied: In an early work on "Automatic facial expression analysis survey" micro-movement spotting Polikovsky et al.
In their later work Polikovsky and Kameda,a tracking algorithm is applied to track the facial points that had been manually detected at the first frame throughout the whole sequence. To prevent the hassle of manually detecting the facial points, majority of the recent works Davison et al.
Instead of the detection for the whole Automatic facial expression analysis survey of facial images, the facial points are only detected at the first frame and fixed in the consecutive frames with the assumption that these points will only change minimally due to the subtleness of MEs.
Facial deformable models can be roughly separated into two main categories: The former applies holistic texture-based facial representation for the generic face fitting scenario; and the latter uses the local image patches around the landmark points for the face fitting scenario.
Although the holistic-based approaches are able to achieve impressive registration quality, these representations unfaithfully locate facial landmarks in unseen images, when target individuals are not included in the Automatic facial expression analysis survey set. As a result, part based models which circumvent several drawbacks of holistic-based methods, are more frequently employed in locating facial landmarks in recent years Asthana et al.
The ASM applies Automatic facial expression analysis survey constraints and searches locally for each point's best location; whereas DRMF learns the variation in appearance on a set of template regions surrounding individual features and updates the shape model accordingly; as for CLM, it learns a model of shape and texture variation from a template similar to active appearance modelsbut the texture is sampled in patches around individual feature points.
In short, the DRMF is computationally lighter than its counterparts. Part based approaches mainly rely on optimization strategies to approximate the responses map through simple parametric representations. However, some ambiguities still result due to the landmark's small support region and imperfect detectors.
To maximize over the KDE, the mean-shift algorithm was applied. The efficacy of the method Zhang et al.
In ME spotting research, very few works applied tracking to the landmark points. This could be due to the sufficiency of landmark detection algorithms used since MEs movements are very minute or that general assumptions have been made to fix the location the detected landmarks points. The two tracking algorithms that were reportedly used in a few facial ME spotting works Polikovsky and Kameda, ; Moilanen Automatic facial expression analysis survey al.
Image registration is the process of aligning two images—the reference and sensed images, geometrically. In Automatic facial expression analysis survey facial ME spotting pipeline, registration techniques are applied onto the faces to remove large head translations and rotations that might affect the spotting task.
Generally, registration techniques can be separated into two major categories: In each of the approaches, either global mapping functions or local mapping functions are applied to transform the the sensed image to be as close as the reference image. This approach bypasses the need for landmark points, albeit some restriction to only shift and small rotations between the images Zitova and Flusser, "Automatic facial expression analysis survey" the work by Davison et al.
This method calculates the cross-correlation of the sensed and reference images before finding the peak, which in turn is used to find the translation between the sensed and reference images. The Automatic Facial Expression Recognition has been one of the latest Previous survey on the automatic analysis of Facial Expressions have discussed. Automatic facial expression recognition: A survey based on feature include human-computer interfaces, human emotion analysis, awareness systems.
Automatic Facial Experssion Analysis. Project: $DO B. Fasel and J. Luettin, " Automatic facial expression analysis: A survey," Pattern Recognition, vol. 36, no.
Exceeding the end few years, automatic facial micro-expression judgement has garnered increasing prominence from experts across disparate disciplines owing to of its potential applications in different fields such as clinical diagnosis, forensic investigation and security systems.
Advances in computer algorithms and video acquisition technology have rendered machine scrutiny of facial micro-expressions uncertain today, in contrast to decades ago when it was pre-eminently the sphere of psychiatrists where scrutiny was essentially manual. In reality, although the study of facial micro-expressions is a well-established enthusiast in trolley, it is still less new from the computational perspective with many provocative problems.
In this surveying, we pourboire a extensive review of state-of-the-art databases and courses for micro-expressions spotting and recognition. Peculiar stages implicated in the automation of these tasks are along described and reviewed at length. In addition, we also meditate on on the challenges and future directions in that growing realm of self-regulating facial micro-expression analysis. In , Ekman and Friesen spotted a quick full-face emotional declaration in a filmed audience which revealed a well-established negative warmth a psychiatric patient was trying to hide from her psychiatrist in progression to persuade that she was no longer suicidal.
This character of facial expressions is called micro-expressions MEs and they were actually essential discovered around Haggard and Isaacs 3 years up front the circumstance happened. In their investigate, Haggard and Isaacs discovered these micromomentary expressions while scanning passage picture films of psychotherapy hours, inquesting for indications of non-verbal communication among patient and therapist. MEs are altogether brief, skilful, and impulsive facial expressions which normally occur when a guy either purposely or unconsciously conceals his or her genuine emotions Ekman and Friesen, ; Ekman, b.
Recent examination by Yan et al. Besides minuscule duration, MEs also secure other outstanding characteristics such as crude intensity and fragmental facial action units where single part of the response units of full-stretched facial expressions are presented Railways redcap and Ten Brinke, ; Yan et al.
Rightful to these three characteristics of the MEs, it is dark for possibly manlike beings to perceive micro-expressions with the naked perspicacity.
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In this survey paper, we first discuss the existing ME corpora. Unlike the aforementioned works which exploited only the single dominant direction of OF in each facial region, Allaert et al. Currently, the two popular evaluation protocols that are widely applied in ME recognition are: To address the potentiality of a false peak, these works Moilanen et al. The Silesian Deception database Radlak et al.
- A SURVEY OF AUTOMATIC FACIAL MICRO-EXPRESSION ANALYSIS: DATABASES, METHODS, AND CHALLENGES
- THE AUTOMATIC FACIAL EXPRESSION RECOGNITION HAS BEEN ONE OF THE LATEST PREVIOUS SURVEY ON THE AUTOMATIC...
- TYPE OF PUBLICATION: IDIAP-RR. CITATION: FASEL-RR NUMBER: IDIAP-RR YEAR: INSTITUTION: IDIAP. NOTE: PUBLISHED IN PATTERN RECOGNITION.
- AUTOMATIC FACIAL EXPRESSION ANALYSIS: A SURVEY - IDIAP PUBLICATIONS
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