Facial Action Coding System

There is a wide debate on the mental state of doubt/uncertainty; one wonders whether it is a predominantly cognitive or emotional state of mind and whether typical facial expressions communicate doubt/uncertainty. To this purpose, through a role playing procedure, a large sample of expressions were collected and afterwards evaluated through a combination of encoding and decoding procedures, including also FACS (Facial Action Coding System) analysis. The results have partially confirmed our hypothesis, identifying two typical facial expressions of doubt/uncertainty, which share the same facial actions in the inferior part of the face and show differential facial actions in the upper face.

Generating extreme appearances such as scared awaiting sweating while happy fit for tears (cry) and blushing (anger and happiness) is the key issue in achieving the high quality facial animation. The effects of sweat, tears, and colors are integrated into a single animation model to create realistic facial expressions of 3D avatar. The physical properties of muscles, emotions, or the fluid properties with sweating and tears initiators are incorporated. The action units (AUs) of facial action coding system are merged with autonomous AUs to create expressions including sadness, anger with blushing, happiness with blushing, and fear. Fluid effects such as sweat and tears are simulated using the particle system and smoothed-particle hydrodynamics (SPH) methods which are combined with facial animation technique to produce complex facial expressions. The effects of oxygenation of the facial skin color appearance are measured using the pulse oximeter system and the 3D skin analyzer. The re...

Automatic Facial Feature Detection is becoming a very important task in applications such as Model Based Video coding, Facial Image Animation, Face Recognition, Facial Emotion Recognition, Intelligent Human Computer Interaction. Most of the approaches for facial feature detection have been proposed which use independent facial feature detectors relying on hand designed filters that aim at segmenting using image properties such as edges, intensity, color, motion. In this paper, A Hierarchical neural based facial feature detection scheme is proposed for robustly and automatically detect a set of user-selected facial features in images that is designed to precisely locate fine features in faces of variable size and appearance.. The proposed system comprises two successive stages: Generalized Rapid Transformation (GRT) and Normalization.

Most stroke patients usually have problems in communication and body movement, such as speaking, sitting, walking, and picking up items. Moreover, the number of caregivers is smaller than the number of stroke patients. Nevertheless, these patients need 24-hour caregivers for the patients’ safety. Therefore, the objective of this research is to determine patterns of anxiety emotion via facial expressions from the sub-structures on the face, such as the inner brow raiser, brow lower, lid raiser, and lip part. Random samples of 360 facial images from horror-thriller movies based on the internet movie database (IMDb) website were selected. Then, 68 facial landmarks for classifying the emotions were applied to each facial image. The differences in these 68 positions before and after the changed emotions were used as the emotional indicators. Furthermore, these different values are applied to implement a decision tree with all the boundaries of the sub-structures disclosed as a suitable c...

There is a wide debate on the mental state of doubt/uncertainty; one wonders whether it is a predominantly cognitive or emotional state of mind and whether typical facial expressions communicate doubt/uncertainty. To this purpose, through a role playing procedure, a large sample of expressions were collected and afterwards evaluated through a combination of encoding and decoding procedures, including also FACS (Facial Action Coding System) analysis. The results have partially confirmed our hypothesis, identifying two typical facial expressions of doubt/uncertainty, which share the same facial actions in the inferior part of the face and show differential facial actions in the upper face.

In this paper, Deterministic Cellular Automata (DCA) based video shot classification and retrieval is proposed. The deterministic 2D Cellular automata model captures the human facial expressions, both spontaneous and posed. The determinism stems from the fact that the facial muscle actions are standardized by the encodings of Facial Action Coding System (FACS) and Action Units (AUs). Based on these encodings,

This paper is aimed at producing a simple mechanism that detects emotion on a person's face from the facial expression as recognised in real-time, the main purpose is to be able to apply a real control panel for Kalman Filter Model (KLM) by tracking the error in frequency while tracing the mood change and detecting error that minimises false positive cases. The initial stage is designed in such a way it automatically detects and tracks the facial feature points with cascaded markers that keep track of the face even while the subject tilts in different directions and positions. This approach is very simple in computer vision applications for most network surveillance and automotive safety. The tracking process uses three modules: (1) face detection (2) feature identification (3) face tracking and detection. The module is signalised as a vision cascade object detector system to detect and locate a face from a video sequence; the detector object uses the KLM inference that trains the classification model for detection. The module can also be used to track moving objects with distinct features. The originality of the process is that all vision point tracker works as a window-based control system. The system is also embedded with error detection and tracking points, with a frequency of 10Hz from every facial angle, and can be used to track even the most subtle point of a blurred image at an estimated geometric transform.

Most psychologists believe that facial behavior through depression differs from facial behavior in the absence of depression, so facial behavior can be utilized as a dependable indicator for spotting depression. Visual depression diagnosis system (VDD) establishes dependents on expressions of the face that are expense-effective and movable. At this work, the VDD system is designed according to the Facial Action Coding System (FACS) to extract features of the face. The key concept of the Facial Action Coding System (FACS) to explain the whole face behavior utilizing Action Units (AUs), every AU is linked to the motion of unique or maybe further face muscles. Six AUs have utilized as depression features; those action units are AUs 4, 5, 6, 7, 10, and 12. The datasets that employed to evaluate the performance of the proposed system are gathered for 125 participants (30 males, 95 females); many of them are among 17-60 years of age. At the final step of the current system, four kinds of ...

BackgroundThis article presents the results of a parallel‐group, non‐randomized, controlled study that evaluated the feasibility of an online training program for improving observer detection of facial pain expression.MethodFifty‐four undergraduate students attended two laboratory sessions interspersed by an intervention period where they were assigned to complete the Index of Facial Pain Expression (IFPE)—an online training environment designed to teach observers to code facial muscle movements associated with pain—or a no‐contact control. Participants completed questionnaires during the first session and watched parallel versions of the Sensitivity to Expression of Pain (STEP) test during laboratory sessions. STEP tests contained excerpts of facial expressions taken from patients with shoulder pain. Reliability of coding following the IFPE was measured. Signal detection methods were applied to pain ratings to the STEP tests to calculate measures of sensitivity and response bias to...

Facial motion analysis is a research field with many practical applications, and has been strongly developed in the last years. However, most effort has been focused on the recognition of basic facial expressions of emotion and neglects the analysis of facial motions related to non-verbal communication signals. This paper focuses on the classification of facial expressions that are of the utmost importance in sign languages (Grammatical Facial Expressions) but also present in expressive spoken language. We have collected a dataset of Spanish Sign Language sentences and extracted the intervals for three types of Grammatical Facial Expressions: negation, closed queries and open queries. A study of several deep learning models using different input features on the collected dataset (LSE_GFE) and an external dataset (BUHMAP) shows that GFEs can be learned reliably with Graph Convolutional Networks simply fed with face landmarks.

We propose a novel approach based on machine learning to simulate facial expressions related to physical activity. Because of the various factors they involve, such as psychological and biomechanical, facial expressions are complex to model. While facial performance capture provides the best results, it is costly and difficult to use for real-time interaction during intense physical activity. A number of methods exist to automate facial animation related to speech or emotion, but there are no methods to automate facial expressions related to physical activity. This leads to unrealistic 3D characters, especially when performing intense physical activity. This research highlights the link between physical activity and facial expression, and to propose a data-driven approach providing realistic facial expressions, while leaving creative control. First, biological, mechanical, and facial expression data are captured. This information is then used to train regression trees and support ve...

Parkinson’s disease (PD) is a neurological disorder that mainly affects the motor system. Among other symptoms, hypomimia is considered one of the clinical hallmarks of the disease. Despite its great impact on patients’ quality of life, it remains still under-investigated. The aim of this work is to provide a quantitative index for hypomimia that can distinguish pathological and healthy subjects and that can be used in the classification of emotions. A face tracking algorithm was implemented based on the Facial Action Coding System. A new easy-to-interpret metric (face mobility index, FMI) was defined considering distances between pairs of geometric features and a classification based on this metric was proposed. Comparison was also provided between healthy controls and PD patients. Results of the study suggest that this index can quantify the degree of impairment in PD and can be used in the classification of emotions. Statistically significant differences were observed for all emo...

A large number of publications have focused on the study of pain expressions. Despite the growing knowledge, the availability of pain-related face databases is still very scarce compared with other emotional facial expressions. The Pain E-Motion Faces Database (PEMF) is a new open-access database currently consisting of 272 micro-clips of 68 different identities. Each model displays one neutral expression and three pain-related facial expressions: posed, spontaneous-algometer and spontaneous-CO2 laser. Normative ratings of pain intensity, valence and arousal were provided by students of three different European universities. Six independent coders carried out a coding process on the facial stimuli based on the Facial Action Coding System (FACS), in which ratings of intensity of pain, valence and arousal were computed for each type of facial expression. Gender and age effects of models across each type of micro-clip were also analysed. Additionally, participants’ ability to discrimin...

BackgroundThere is a broad range of evidence on optimism dampening the pain experience, as assessed by subjective self‐report. Facial expression of pain conveys supplementary information about the pain experience, is an integral part of pain communication and assists psychosocial pain coping. Nevertheless, the effect of induced optimism on facial activity during pain has to our knowledge not been examined.MethodsIn our experiment, 40 healthy participants underwent two blocks of thermal stimulation containing phasic non‐painful and painful stimuli. Between the two blocks, the Best Possible Self imagery and writing task was performed to induce situational optimism, while a control group wrote about their typical day. Facial activity and self‐report ratings of intensity and unpleasantness were recorded. Facial activity was analysed using the Facial Action Coding System.ResultsThe optimism manipulation was successful in increasing state optimism. It did not affect self‐report ratings, b...

BackgroundFacial activity during pain is composed of varying combinations of a few elementary facial responses (so‐called Action Units). A previous study of experimental pain showed that these varying combinations can be clustered into distinct facial activity patterns of pain. In the present study, we examined whether comparable facial activity patterns can also be identified among people suffering from clinical pain; namely, shoulder pain.MethodsFacial expressions of patients suffering from shoulder pain (N = 126) were recorded while twice undergoing a battery of passive range‐of‐motion tests to their affected limbs (UNBC‐McMaster Shoulder Pain Expression Archive Database), which elicited peaks of acute pain. Facial expressions were analysed using the Facial Action Coding System to extract facial Action Units (AUs). Hierarchical cluster analyses were used to look for characteristic combinations of these AUs.ResultsCluster analyses revealed four distinct activity patterns during pa...

This work presents an analysis of the efficiency and effectiveness of a Video-Based Pain Monitoring System running on a Raspberry selected because it is a cheap device that can be easily carried around. The objective of the evaluated system is to allow the assessment of pain based on two characteristics: Heart Rate (HR) and facial expressions detected through the Facial Action Coding System (FACS). To measure HR an Eulerian Video Magnification (EVM) based method was implemented. EVM is one of the main current approaches to measure HR by Remote PhotoPlethysmoGraphy. FACS was used to detect pain intensity with the Prkachin and Solomon Pain Intensity (PSPI) equation which is one of the most used approaches to detect pain intensity based on facial features. To identify the PSPI value we trained a Regression Neural Network (RNN) with the UNBC-McMaster database. The experimental results demonstrate the strengths and limitations of the evaluated system.

In this paper we present a fuzzy reasoning model and a designed system for Recognition of Facial Expressions, which can measure and recognize the intensity of basic or non-prototypical emotions. The proposed model operates with encoded facial deformations described in terms of either Ekmans Action Units (AUs) or Facial Animation Parameters (FAPs) of MPEG-4 standard and provides recognition of facial expression using a knowledge base implemented on knowledge acquisition and ontology editor Protege. It allows modeling of facial features obtained from geometric parameters coded by AUs - FAPs and from a set of rules required for classification of measured expressions. This paper also presents a designed framework for fuzzyfication of input variables of a fuzzy classifier based on statistical analysis of emotions expressed in video records of standard Cohn-Kanade's and Pantics MMI face databases. The proposed system designed according to developed model has been tested in order to ev...

This study investigates the facial features of schadenfreude laughter in historic illustrations by applying the Facial Action Coding System and assesses the decoding by naive subjects. Results show that while the encoding of schadenfreude laughter is heterogeneous, schadenfreude is decoded when the facial expression unites markers of joy (Duchenne Display, consisting of the orbicularis oculi pars orbitalis muscle and the zygomatic major muscle), as well as markers of negative emotions (e.g., brow lowering), or in one case, where the initially categorized schadenfreude illustration contained markers distorting the expression of joy (e.g., frowning and the lowering of lip corners). These findings support the hypothesis that schadenfreude may be expressed by a morphologically distinct blend of a positive and a negative emotion, or is expressed by joyful laughter (with the expression being modulated due to social desirability).

That basic emotions produce a facial signal would—if true—provide a foundation for a science of emotion. Here, random-effects meta-analyses tested whether happiness, sadness, anger, disgust, fear, and surprise each co-occurs with its predicted facial signal. The first meta-analysis examined only those studies that measured full expressions through Facial Actions Coding System (FACS). Average co-occurrence effect size was .13. The second meta-analysis included both full and partial expressions, as measured by FACS or another system. Average co-occurrence effect size rose to .23. A third meta-analysis estimated the Pearson correlation between intensity of the reported emotion and intensity of the predicted facial expression. Average correlation was .30. Overall, co-occurrence and correlation were greatest for disgust, least for surprise. What are commonly known as the six classic basic emotions do not reliably co-occur with their predicted facial signal. Heterogeneity between samples ...