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

Abstract

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.

Effective recognition of facial micro-expressions with video motion magnification

“…In the pattern recognition and machine vision community, research started on posed datasets [14,20,21,28] i.e. where subjects were asked to show how they are feeling; it was subsequently felt that spontaneous datasets [3,22] capture more realistically the actual emotional situations experienced by subjects i.e. subjects were induced to naturally express their feelings rather than being asked to show a specific feeling.…”

Wearable Assistive Technologies for Autism: Opportunities and Challenges

“…One of the obstacles might be the scarcity of training data in naturalistic situations. It is a notoriously expensive undertaking to collect such a dataset, not to mention annotating them in an objective, reliable and quantitative way [29].…”

Real Time Multimodal Emotion Recognition System using Facial Landmarks and Hand over Face Gestures

“…in one sixth of a second. For Facial Landmark detection we used MUG [19] database, for benchmarking frame rate we used AMFED [20] which had around 242 videos captured in real world scenarios. MUG database consists of image sequences of 86 subjects performing facial expressions.…”