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/

Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration.

In animal research, automation of affective states recognition has so far mainly addressed pain in a few species. Emotional states remain uncharted territories, especially in dogs, due to the complexity of their facial morphology and expressions. This study contributes to fill this gap in two aspects. First, it is the first to address dog emotional states using a dataset obtained in a controlled experimental setting, including videos from (n = 29) Labrador Retrievers assumed to be in two experimentally induced emotional states: negative (frustration) and positive (anticipation). The dogs' facial expressions were measured using the Dogs Facial Action Coding System (DogFACS). Two different approaches are compared in relation to our aim: (1) a DogFACS-based approach with a two-step pipeline consisting of (i) a DogFACS variable detector and (ii) a positive/negative state Decision Tree classifier; (2) An approach using deep learning techniques with no intermediate representation. The approaches reach accuracy of above 71% and 89%, respectively, with the deep learning approach performing better. Secondly, this study is also the first to study explainability of AI models in the context of emotion in animals. The DogFACS-based approach provides decision trees, that is a mathematical representation which reflects previous findings by human experts in relation to certain facial expressions (DogFACS variables) being correlates of specific emotional states. The deep learning approach offers a different, visual form of explainability in the form of heatmaps reflecting regions of focus of the network's attention, which in some cases show focus clearly related to the nature of particular DogFACS variables. These heatmaps may hold the key to novel insights on the sensitivity of the network to nuanced pixel patterns reflecting information invisible to the human eye

Automatic recognition of micro-expressions using local binary patterns on three orthogonal planes and extreme learning machine

A dissertation submitted in fullment of the requirements for the degree of Master of Science to the Faculty of Science, University of the Witwatersrand, Johannesburg, September 2017Recognition of micro-expressions is a growing research area as a result of its application in revealing subtle intention of humans especially under high stake situations. Owing to micro-expressions' short duration and low inten- sity, e orts to train humans in their recognition has resulted in very low performance. The use of temporal methods (on image sequences) and static methods (on apex frames) were explored for feature extraction. Supervised machine learning algorithms which include Support Vector Machines (SVM) and Extreme Learning Machines (ELM) were used for the purpose of classi- cation. Extreme learning machines which has the ability to learn fast was compared with SVM which acted as the baseline model. For experimentation, samples from Chinese Academy of Micro-expressions (CASME II) database were used. Results revealed that use of temporal features outperformed the use of static features for micro-expression recognition on both SVM and ELM models. Static and temporal features gave an average testing accuracy of 94.08% and 97.57% respectively for ve classes of micro-expressions us- ing ELM model. Signi cance test carried out on these two average means suggested that temporal features outperformed static features using ELM. Comparison between SVM and ELM learning time also revealed that ELM learns faster than SVM. For the ve selected micro-expression classes, an av- erage training time of 0.3405 seconds was achieved for SVM while an average training time of 0.0409 seconds was achieved for ELM. Hence we can sug- gest that micro-expressions can be recognised successfully by using temporal features and a machine learning algorithm that has a fast learning speed.MT201

Exploring Temporal Patterns in Classifying Frustrated and Delighted Smiles

We create two experimental situations to elicit two affective states: frustration, and delight. In the first experiment, participants were asked to recall situations while expressing either delight or frustration, while the second experiment tried to elicit these states naturally through a frustrating experience and through a delightful video. There were two significant differences in the nature of the acted versus natural occurrences of expressions. First, the acted instances were much easier for the computer to classify. Second, in 90 percent of the acted cases, participants did not smile when frustrated, whereas in 90 percent of the natural cases, participants smiled during the frustrating interaction, despite self-reporting significant frustration with the experience. As a follow up study, we develop an automated system to distinguish between naturally occurring spontaneous smiles under frustrating and delightful stimuli by exploring their temporal patterns given video of both. We extracted local and global features related to human smile dynamics. Next, we evaluated and compared two variants of Support Vector Machine (SVM), Hidden Markov Models (HMM), and Hidden-state Conditional Random Fields (HCRF) for binary classification. While human classification of the smile videos under frustrating stimuli was below chance, an accuracy of 92 percent distinguishing smiles under frustrating and delighted stimuli was obtained using a dynamic SVM classifier.MIT Media Lab ConsortiumProcter & Gamble Compan

Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

Artificial Intelligence Tools for Facial Expression Analysis.

Inner emotions show visibly upon the human face and are understood as a basic guide to an individual’s inner world. It is, therefore, possible to determine a person’s attitudes and the effects of others’ behaviour on their deeper feelings through examining facial expressions. In real world applications, machines that interact with people need strong facial expression recognition. This recognition is seen to hold advantages for varied applications in affective computing, advanced human-computer interaction, security, stress and depression analysis, robotic systems, and machine learning. This thesis starts by proposing a benchmark of dynamic versus static methods for facial Action Unit (AU) detection. AU activation is a set of local individual facial muscle parts that occur in unison constituting a natural facial expression event. Detecting AUs automatically can provide explicit benefits since it considers both static and dynamic facial features. For this research, AU occurrence activation detection was conducted by extracting features (static and dynamic) of both nominal hand-crafted and deep learning representation from each static image of a video. This confirmed the superior ability of a pretrained model that leaps in performance. Next, temporal modelling was investigated to detect the underlying temporal variation phases using supervised and unsupervised methods from dynamic sequences. During these processes, the importance of stacking dynamic on top of static was discovered in encoding deep features for learning temporal information when combining the spatial and temporal schemes simultaneously. Also, this study found that fusing both temporal and temporal features will give more long term temporal pattern information. Moreover, we hypothesised that using an unsupervised method would enable the leaching of invariant information from dynamic textures. Recently, fresh cutting-edge developments have been created by approaches based on Generative Adversarial Networks (GANs). In the second section of this thesis, we propose a model based on the adoption of an unsupervised DCGAN for the facial features’ extraction and classification to achieve the following: the creation of facial expression images under different arbitrary poses (frontal, multi-view, and in the wild), and the recognition of emotion categories and AUs, in an attempt to resolve the problem of recognising the static seven classes of emotion in the wild. Thorough experimentation with the proposed cross-database performance demonstrates that this approach can improve the generalization results. Additionally, we showed that the features learnt by the DCGAN process are poorly suited to encoding facial expressions when observed under multiple views, or when trained from a limited number of positive examples. Finally, this research focuses on disentangling identity from expression for facial expression recognition. A novel technique was implemented for emotion recognition from a single monocular image. A large-scale dataset (Face vid) was created from facial image videos which were rich in variations and distribution of facial dynamics, appearance, identities, expressions, and 3D poses. This dataset was used to train a DCNN (ResNet) to regress the expression parameters from a 3D Morphable Model jointly with a back-end classifier