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/

Macro-and Micro-Expressions Facial Datasets: A Survey

Automatic facial expression recognition is essential for many potential applications. Thus, having a clear overview on existing datasets that have been investigated within the framework of face expression recognition is of paramount importance in designing and evaluating effective solutions, notably for neural networks-based training. In this survey, we provide a review of more than eighty facial expression datasets, while taking into account both macro-and micro-expressions. The proposed study is mostly focused on spontaneous and in-the-wild datasets, given the common trend in the research is that of considering contexts where expressions are shown in a spontaneous way and in a real context. We have also provided instances of potential applications of the investigated datasets, while putting into evidence their pros and cons. The proposed survey can help researchers to have a better understanding of the characteristics of the existing datasets, thus facilitating the choice of the data that best suits the particular context of their application

AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit

In this paper we introduce AFFDEX 2.0 - a toolkit for analyzing facial expressions in the wild, that is, it is intended for users aiming to; a) estimate the 3D head pose, b) detect facial Action Units (AUs), c) recognize basic emotions and 2 new emotional states (sentimentality and confusion), and d) detect high-level expressive metrics like blink and attention. AFFDEX 2.0 models are mainly based on Deep Learning, and are trained using a large-scale naturalistic dataset consisting of thousands of participants from different demographic groups. AFFDEX 2.0 is an enhanced version of our previous toolkit [1], that is capable of tracking efficiently faces at more challenging conditions, detecting more accurately facial expressions, and recognizing new emotional states (sentimentality and confusion). AFFDEX 2.0 can process multiple faces in real time, and is working across the Windows and Linux platforms.Comment: Accepted at the FG2023 conferenc

Learning Grimaces by Watching TV

Differently from computer vision systems which require explicit supervision, humans can learn facial expressions by observing people in their environment. In this paper, we look at how similar capabilities could be developed in machine vision. As a starting point, we consider the problem of relating facial expressions to objectively measurable events occurring in videos. In particular, we consider a gameshow in which contestants play to win significant sums of money. We extract events affecting the game and corresponding facial expressions objectively and automatically from the videos, obtaining large quantities of labelled data for our study. We also develop, using benchmarks such as FER and SFEW 2.0, state-of-the-art deep neural networks for facial expression recognition, showing that pre-training on face verification data can be highly beneficial for this task. Then, we extend these models to use facial expressions to predict events in videos and learn nameable expressions from them. The dataset and emotion recognition models are available at http://www.robots.ox.ac.uk/~vgg/data/facevalueComment: British Machine Vision Conference (BMVC) 201

FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation

Facial expression analysis based on machine learning requires large number of well-annotated data to reflect different changes in facial motion. Publicly available datasets truly help to accelerate research in this area by providing a benchmark resource, but all of these datasets, to the best of our knowledge, are limited to rough annotations for action units, including only their absence, presence, or a five-level intensity according to the Facial Action Coding System. To meet the need for videos labeled in great detail, we present a well-annotated dataset named FEAFA for Facial Expression Analysis and 3D Facial Animation. One hundred and twenty-two participants, including children, young adults and elderly people, were recorded in real-world conditions. In addition, 99,356 frames were manually labeled using Expression Quantitative Tool developed by us to quantify 9 symmetrical FACS action units, 10 asymmetrical (unilateral) FACS action units, 2 symmetrical FACS action descriptors and 2 asymmetrical FACS action descriptors, and each action unit or action descriptor is well-annotated with a floating point number between 0 and 1. To provide a baseline for use in future research, a benchmark for the regression of action unit values based on Convolutional Neural Networks are presented. We also demonstrate the potential of our FEAFA dataset for 3D facial animation. Almost all state-of-the-art algorithms for facial animation are achieved based on 3D face reconstruction. We hence propose a novel method that drives virtual characters only based on action unit value regression of the 2D video frames of source actors.Comment: 9 pages, 7 figure

Real Time Facial Expression Recognition Using Webcam and SDK Affectiva

Facial expression is an essential part of communication. For this reason, the issue of human emotions evaluation using a computer is a very interesting topic, which has gained more and more attention in recent years. It is mainly related to the possibility of applying facial expression recognition in many fields such as HCI, video games, virtual reality, and analysing customer satisfaction etc. Emotions determination (recognition process) is often performed in 3 basic phases: face detection, facial features extraction, and last stage - expression classification. Most often you can meet the so-called Ekman’s classification of 6 emotional expressions (or 7 - neutral expression) as well as other types of classification - the Russell circular model, which contains up to 24 or the Plutchik’s Wheel of Emotions. The methods used in the three phases of the recognition process have not only improved over the last 60 years, but new methods and algorithms have also emerged that can determine the ViolaJones detector with greater accuracy and lower computational demands. Therefore, there are currently various solutions in the form of the Software Development Kit (SDK). In this publication, we point to the proposition and creation of our system for real-time emotion classification. Our intention was to create a system that would use all three phases of the recognition process, work fast and stable in real time. That’s why we’ve decided to take advantage of existing Affectiva SDKs. By using the classic webcamera we can detect facial landmarks on the image automatically using the Software Development Kit (SDK) from Affectiva. Geometric feature based approach is used for feature extraction. The distance between landmarks is used as a feature, and for selecting an optimal set of features, the brute force method is used. The proposed system uses neural network algorithm for classification. The proposed system recognizes 6 (respectively 7) facial expressions, namely anger, disgust, fear, happiness, sadness, surprise and neutral. We do not want to point only to the percentage of success of our solution. We want to point out the way we have determined this measurements and the results we have achieved and how these results have significantly influenced our future research direction

Facial Expression Recognition from World Wild Web

Recognizing facial expression in a wild setting has remained a challenging task in computer vision. The World Wide Web is a good source of facial images which most of them are captured in uncontrolled conditions. In fact, the Internet is a Word Wild Web of facial images with expressions. This paper presents the results of a new study on collecting, annotating, and analyzing wild facial expressions from the web. Three search engines were queried using 1250 emotion related keywords in six different languages and the retrieved images were mapped by two annotators to six basic expressions and neutral. Deep neural networks and noise modeling were used in three different training scenarios to find how accurately facial expressions can be recognized when trained on noisy images collected from the web using query terms (e.g. happy face, laughing man, etc)? The results of our experiments show that deep neural networks can recognize wild facial expressions with an accuracy of 82.12%

Automatic User-Video Metrics Creations From Emotion Detection

In this digital era, digital content especially video, is increasing in number from time to time. Typically, a video service provider like Youtube will perform video analysis based on the video content such as colours, textures, shapes, and other features that exist in video content. The result of this analysis was used to understand user preference and to personalize video for each user. With technological developments, especially in Machine Learning and Computer Vision technology, video analysis can be based on other things beyond the video. In this context, it is the audience's impression. Thus, with the analysis of audience impressions in real-time, it is expected that the video can be analysed using the emotion parameters of the audience while the video is playing, and this can be done automatically and real-time. This system generates impression statistic for each video which concluded from every user who has watched the video and save those data in the database. Method used to analyse the result is by recruiting respondent and give some questionnaires. Respondents were asked to watch some videos and were asked to compare the impression metric which created by the system with user's real impression. The result shos that the automatic video-metric creation from emotion detection has been able to measure user's impression of the video with more than 80% accuracy stated by 75% of 20 respondents of the survey