Results 1 - 10
of
21
Comprehensive database for facial expression analysis
- in Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition
"... Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, ..."
Abstract
-
Cited by 259 (34 self)
- Add to MetaCart
Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, which includes level of description, transitions among expression, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity, image characteristics, and relation to non-verbal behavior. We then present the CMU-Pittsburgh AU-Coded Face Expression Image Database, which currently includes 2105 digitized image sequences from 182 adult subjects of varying ethnicity, performing multiple tokens of most primary FACS action units. This database is the most comprehensive test-bed to date for comparative studies of facial expression analysis. 1.
Automated Face Analysis by Feature Point Tracking Has High Concurrent Validity with Manual FACS Coding
, 1999
"... The face is a rich source of information about human behavior. Available methods for coding facial displays, however, are human-observer dependent, labor intensive, and difficult to standardize. To enable rigorous and efficient quantitative measurement of facial displays, we have developed an automa ..."
Abstract
-
Cited by 67 (28 self)
- Add to MetaCart
The face is a rich source of information about human behavior. Available methods for coding facial displays, however, are human-observer dependent, labor intensive, and difficult to standardize. To enable rigorous and efficient quantitative measurement of facial displays, we have developed an automated method of facial display analysis. In this report we compare the results with those of manual FACS (Facial Action Coding System, Ekman & Friesen, 1978a) coding. One hundred university students were videotaped while performing a series of facial displays. The image sequences were coded from videotape by certified FACS coders. Fifteen action units and action unit combinations that occurred a minimum of 25 times were selected for automated analysis. Facial features were automatically tracked in digitized image sequences using a hierarchical algorithm for estimating optical flow. The measurements were normalized for variation in position, orientation, and scale. The image sequences were rand...
Human Computing and Machine Understanding of Human Behavior: A Survey
- SURVEY, PROC. ACM INT’L CONF. MULTIMODAL INTERFACES
, 2006
"... A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should b ..."
Abstract
-
Cited by 54 (25 self)
- Add to MetaCart
A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior.
Automatic Recognition of Facial Actions in Spontaneous Expressions
"... Abstract — Spontaneous facial expressions differ from posed expressions in both which muscles are moved, and in the dynamics of the movement. Advances in the field of automatic facial expression measurement will require development and assessment on spontaneous behavior. Here we present preliminary ..."
Abstract
-
Cited by 45 (7 self)
- Add to MetaCart
Abstract — Spontaneous facial expressions differ from posed expressions in both which muscles are moved, and in the dynamics of the movement. Advances in the field of automatic facial expression measurement will require development and assessment on spontaneous behavior. Here we present preliminary results on a task of facial action detection in spontaneous facial expressions. We employ a user independent fully automatic system for real time recognition of facial actions from the Facial Action Coding System (FACS). The system automatically detects frontal faces in the video stream and coded each frame with respect to 20 Action units. The approach applies machine learning methods such as support vector machines and AdaBoost, to texture-based image representations. The output margin for the learned classifiers predicts action unit intensity. Frame-by-frame intensity measurements will enable investigations into facial expression dynamics which were previously intractable by human coding. I.
Is there universal recognition of emotion from facial expression? A review of the cross-cultural studies
- Psychological Bulletin
, 1994
"... Emotions are universally recognized from facial expressions—or so it has been claimed. To support that claim, research has been carried out in various modern cultures and in cultures relatively isolated from Western influence. A review of the methods used in that research raises questions of its eco ..."
Abstract
-
Cited by 42 (0 self)
- Add to MetaCart
Emotions are universally recognized from facial expressions—or so it has been claimed. To support that claim, research has been carried out in various modern cultures and in cultures relatively isolated from Western influence. A review of the methods used in that research raises questions of its ecological, convergent, and internal validity. Forced-choice response format, within-subject design, preselected photographs of posed facial expressions, and other features of method are each problematic. When they are altered, less supportive or nonsupportive results occur. When they are combined, these method factors may help to shape the results. Facial expressions and emotion labels are probably associated, but the association may vary with culture and is loose enough to be consistent with various alternative accounts, 8 of which are discussed. "Everyone knows that grief involves a gloomy and joy a cheerful countenance.... There are characteristic facial expressions which are observed to accompany anger, fear, erotic excitement, and all the other passions " (Aristotle, nd/1913, pp. 805, 808). Aristotle was not proposing a new idea but was cataloging what was known on the topic of physiognomy. The theory was that a person's physical appearance, especially in the face, reveals deeper characteristics: Poor proportions reveal a rogue, soft hair a coward, and a smile a happy person.' Today, few psychologists share Aristotle's belief about the meaning of poor proportions or soft hair, but many share his beliefs about facial expression and emotion. Oatley and Jenkins (1992) observed, "By far the most extensive body of data in the field of human emotions is that on facial expressions of emotion" (p. 67). Recent reviews of those data (see Table 1) agree that the face reveals emotion in a way that is universally understood: Happiness, surprise, fear, anger, contempt, disgust, and sadness—these seven emotions, plus or minus two, are recognized from facial expressions by all human beings, regardless of their cultural background.
Automatic Recognition of Facial Expressions Using Hidden Markov Models and Estimation of Expression Intensity
, 1998
"... Signature Professor Ching-Chung Li Signature Professor Takeo Kanade Signature Professor Jeffrey F. Cohn AUTOMATIC RECOGNITION OF FACIAL EXPRESSIONS USING HIDDEN MARKOV MODELS AND ESTIMATION OF EXPRSSION INTENSITY Jenn-Jier James Lien, Ph.D. Facial expressions provide sensitive cues about emotional r ..."
Abstract
-
Cited by 25 (1 self)
- Add to MetaCart
Signature Professor Ching-Chung Li Signature Professor Takeo Kanade Signature Professor Jeffrey F. Cohn AUTOMATIC RECOGNITION OF FACIAL EXPRESSIONS USING HIDDEN MARKOV MODELS AND ESTIMATION OF EXPRSSION INTENSITY Jenn-Jier James Lien, Ph.D. Facial expressions provide sensitive cues about emotional responses and play a major role in the study of psychological phenomena and the development of nonverbal communication. Facial expressions regulate social behavior, signal communicative intent, and are related to speech production. Most facial expression recognition systems focus on v only six basic expressions. In everyday life, however, these six basic expressions occur relatively infrequently, and emotion or intent is more often communicated by subtle changes in one or two discrete features, such as tightening of the lips which may communicate anger. Humans are capable of producing thousands of expressions that vary in complexity, intensity, and meaning. The objective of this dissertati...
Feature-Point Tracking by Optical Flow Discriminates Subtle Differences in Facial Expression
- In Proceedings International Conference on Automatic Face and Gesture Recognition
, 1998
"... Current approaches to automated analysis have focused on a small set of prototypic expressions (e.g., joy or anger). Prototypic expressions occur infrequently in everyday life, however, and emotion expression is far more varied. To capture the full range of emotion expression, automated discriminati ..."
Abstract
-
Cited by 25 (1 self)
- Add to MetaCart
Current approaches to automated analysis have focused on a small set of prototypic expressions (e.g., joy or anger). Prototypic expressions occur infrequently in everyday life, however, and emotion expression is far more varied. To capture the full range of emotion expression, automated discrimination of fine-grained changes in facial expression is needed. We developed and implemented an optical-flow based approach (feature point tracking) that is sensitive to subtle changes in facial expression. In image sequences from 100 young adults, action units and action unit combinations in the brow and mouth regions were selected for analysis if they occurred a minimum of 25 times in the image database. Selected facial features were automatically tracked using a hierarchical algorithm for estimating optical flow. Images sequences were randomly divided into training and test sets. Feature point tracking demonstrated high concurrent validity with human coding using the Facial Action Coding System (FACS). 1.
Subtly Different Facial Expression Recognition And Expression Intensity Estimation
, 1998
"... We have developed a computer vision system, including both facial feature extraction and recognition, that automatically discriminates among subtly different facial expressions. Expression classification is based on Facial Action Coding System (FACS) action units (AUs), and discrimination is perform ..."
Abstract
-
Cited by 21 (4 self)
- Add to MetaCart
We have developed a computer vision system, including both facial feature extraction and recognition, that automatically discriminates among subtly different facial expressions. Expression classification is based on Facial Action Coding System (FACS) action units (AUs), and discrimination is performed using Hidden Markov Models (HMMs). Three methods are developed to extract facial expression information for automatic recognition. The first method is facial feature point tracking using a coarse-to-fine pyramid method. This method is sensitive to subtle feature motion and is capable of handling large displacements with sub-pixel accuracy. The second method is dense flow tracking together with principal component analysis (PCA), where the entire facial motion information per frame is compressed to a lowdimensional weight vector. The third method is high gradient component (i.e., furrow) analysis in the spatiotemporal domain, which exploits the transient variation associated with the facia...
A prototype for automatic recognition of spontaneous facial actions
- in S. Becker and K. Obermayer (Eds.) Advances in Neural Information Processing Systems
, 2003
"... We present ongoing work on a project for automatic recognition of spontaneous facial actions. Spontaneous facial expressions differ substantially from posed expressions, similar to how continuous, spontaneous speech differs from isolated words produced on command. Previous methods for automatic faci ..."
Abstract
-
Cited by 19 (5 self)
- Add to MetaCart
We present ongoing work on a project for automatic recognition of spontaneous facial actions. Spontaneous facial expressions differ substantially from posed expressions, similar to how continuous, spontaneous speech differs from isolated words produced on command. Previous methods for automatic facial expression recognition assumed images were collected in controlled environments in which the subjects deliberately faced the camera. Since people often nod or turn their heads, automatic recognition of spontaneous facial behavior requires methods for handling out-of-image-plane head rotations. Here we explore an approach based on 3-D warping of images into canonical views. We evaluated the performance of the approach as a front-end for a spontaneous expression recognition system using support vector machines and hidden Markov models. This system employed general purpose learning mechanisms that can be applied to recognition of any facial movement. The system was tested for recognition of a set of facial actions defined by the Facial Action Coding System (FACS). We showed that 3D tracking and warping followed by machine learning techniques directly applied to the warped images, is a viable and promising technology for automatic facial expression recognition. One exciting aspect of the approach presented here is that information about movement dynamics emerged out of filters which were derived from the statistics of images. 1

