Foundations of human computing: Facial expression and emotion

ABSTRACT Many people believe that emotions and subjective feelings are one and the same and that a goal of human-centered computing is emotion recognition. The first belief is outdated; the second mistaken. For human-centered computing to succeed, a different way of thinking is needed. Emotions are species-typical patterns that evolved because of their value in addressing fundamental life task

Investigating Spontaneous Facial Action Recognition through AAM Representations of the Face

The Facial Action Coding System (FACS) [Ekman et al., 2002] is the leading method for measuring facial movement in behavioral science. FACS has been successfully applied, but not limited to, identifying the differences between simulated and genuine pain, differences betweenwhen people are telling the truth versus lying, and differences between suicidal an

Predicting Ad Liking and Purchase Intent: Large-Scale Analysis of Facial Responses to Ads

Billions of online video ads are viewed every month. We present a large-scale analysis of facial responses to video content measured over the Internet and their relationship to marketing effectiveness. We collected over 12,000 facial responses from 1,223 people to 170 ads from a range of markets and product categories. The facial responses were automatically coded frame-by-frame. Collection and coding of these 3.7 million frames would not have been feasible with traditional research methods. We show that detected expressions are sparse but that aggregate responses reveal rich emotion trajectories. By modeling the relationship between the facial responses and ad effectiveness, we show that ad liking can be predicted accurately (ROC AUC = 0.85) from webcam facial responses. Furthermore, the prediction of a change in purchase intent is possible (ROC AUC = 0.78). Ad liking is shown by eliciting expressions, particularly positive expressions. Driving purchase intent is more complex than just making viewers smile: peak positive responses that are immediately preceded by a brand appearance are more likely to be effective. The results presented here demonstrate a reliable and generalizable system for predicting ad effectiveness automatically from facial responses without a need to elicit self-report responses from the viewers. In addition we can gain insight into the structure of effective ads.MIT Media Lab ConsortiumNEC CorporationMAR

General Strategies for Isolating the Genes Encoding Type I Collagen and for Characterizing Mutations Which Produce Osteogenesis Imperfecta a

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73326/1/j.1749-6632.1988.tb55325.x.pd

Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected In-the-Wild

Computer classification of facial expressions requires large amounts of data and this data needs to reflect the diversity of conditions seen in real applications. Public datasets help accelerate the progress of research by providing researchers with a benchmark resource. We present a comprehensively labeled dataset of ecologically valid spontaneous facial responses recorded in natural settings over the Internet. To collect the data, online viewers watched one of three intentionally amusing Super Bowl commercials and were simultaneously filmed using their webcam. They answered three self-report questions about their experience. A subset of viewers additionally gave consent for their data to be shared publicly with other researchers. This subset consists of 242 facial videos (168,359 frames) recorded in real world conditions. The dataset is comprehensively labeled for the following: 1) frame-by-frame labels for the presence of 10 symmetrical FACS action units, 4 asymmetric (unilateral) FACS action units, 2 head movements, smile, general expressiveness, feature tracker fails and gender; 2) the location of 22 automatically detected landmark points; 3) self-report responses of familiarity with, liking of, and desire to watch again for the stimuli videos and 4) baseline performance of detection algorithms on this dataset. This data is available for distribution to researchers online, the EULA can be found at: http://www.affectiva.com/facial-expression-dataset-am-fed/