Machine Analysis of Facial Expressions

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EMPATH: A Neural Network that Categorizes Facial Expressions

There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain

Facial Action Recognition Combining Heterogeneous Features via Multi-Kernel Learning

International audienceThis paper presents our response to the first interna- tional challenge on Facial Emotion Recognition and Analysis. We propose to combine different types of features to automatically detect Action Units in facial images. We use one multi-kernel SVM for each Action Unit we want to detect. The first kernel matrix is computed using Local Gabor Binary Pattern histograms and a histogram intersection kernel. The second kernel matrix is computed from AAM coefficients and an RBF kernel. During the training step, we combine these two types of features using the recently proposed SimpleMKL algorithm. SVM outputs are then averaged to exploit temporal information in the sequence. To eval- uate our system, we perform deep experimentations on several key issues: influence of features and kernel function in histogram- based SVM approaches, influence of spatially-independent in- formation versus geometric local appearance information and benefits of combining both, sensitivity to training data and interest of temporal context adaptation. We also compare our results to those of the other participants and try to explain why our method had the best performance during the FERA challenge

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Automatic analysis of facial actions: a survey

As one of the most comprehensive and objective ways to describe facial expressions, the Facial Action Coding System (FACS) has recently received significant attention. Over the past 30 years, extensive research has been conducted by psychologists and neuroscientists on various aspects of facial expression analysis using FACS. Automating FACS coding would make this research faster and more widely applicable, opening up new avenues to understanding how we communicate through facial expressions. Such an automated process can also potentially increase the reliability, precision and temporal resolution of coding. This paper provides a comprehensive survey of research into machine analysis of facial actions. We systematically review all components of such systems: pre-processing, feature extraction and machine coding of facial actions. In addition, the existing FACS-coded facial expression databases are summarised. Finally, challenges that have to be addressed to make automatic facial action analysis applicable in real-life situations are extensively discussed. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the future of machine recognition of facial actions: what are the challenges and opportunities that researchers in the field face

Out-of-plane action unit recognition using recurrent neural networks

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2015.The face is a fundamental tool to assist in interpersonal communication and interaction between people. Humans use facial expressions to consciously or subconsciously express their emotional states, such as anger or surprise. As humans, we are able to easily identify changes in facial expressions even in complicated scenarios, but the task of facial expression recognition and analysis is complex and challenging to a computer. The automatic analysis of facial expressions by computers has applications in several scientific subjects such as psychology, neurology, pain assessment, lie detection, intelligent environments, psychiatry, and emotion and paralinguistic communication. We look at methods of facial expression recognition, and in particular, the recognition of Facial Action Coding System’s (FACS) Action Units (AUs). Movements of individual muscles on the face are encoded by FACS from slightly different, instant changes in facial appearance. Contractions of specific facial muscles are related to a set of units called AUs. We make use of Speeded Up Robust Features (SURF) to extract keypoints from the face and use the SURF descriptors to create feature vectors. SURF provides smaller sized feature vectors than other commonly used feature extraction techniques. SURF is comparable to or outperforms other methods with respect to distinctiveness, robustness, and repeatability. It is also much faster than other feature detectors and descriptors. The SURF descriptor is scale and rotation invariant and is unaffected by small viewpoint changes or illumination changes. We use the SURF feature vectors to train a recurrent neural network (RNN) to recognize AUs from the Cohn-Kanade database. An RNN is able to handle temporal data received from image sequences in which an AU or combination of AUs are shown to develop from a neutral face. We are recognizing AUs as they provide a more fine-grained means of measurement that is independent of age, ethnicity, gender and different expression appearance. In addition to recognizing FACS AUs from the Cohn-Kanade database, we use our trained RNNs to recognize the development of pain in human subjects. We make use of the UNBC-McMaster pain database which contains image sequences of people experiencing pain. In some cases, the pain results in their face moving out-of-plane or some degree of in-plane movement. The temporal processing ability of RNNs can assist in classifying AUs where the face is occluded and not facing frontally for some part of the sequence. Results are promising when tested on the Cohn-Kanade database. We see higher overall recognition rates for upper face AUs than lower face AUs. Since keypoints are globally extracted from the face in our system, local feature extraction could provide improved recognition results in future work. We also see satisfactory recognition results when tested on samples with out-of-plane head movement, showing the temporal processing ability of RNNs

Dynamic Facial Emotion Recognition Oriented to HCI Applications

Producción CientíficaAs part of a multimodal animated interface previously presented in [38], in this paper we describe a method for dynamic recognition of displayed facial emotions on low resolution streaming images. First, we address the detection of Action Units of the Facial Action Coding System upon Active Shape Models and Gabor filters. Normalized outputs of the Action Unit recognition step are then used as inputs for a neural network which is based on real cognitive systems architecture, and consists on a habituation network plus a competitive network. Both the competitive and the habituation layer use differential equations thus taking into account the dynamic information of facial expressions through time. Experimental results carried out on live video sequences and on the Cohn-Kanade face database show that the proposed method provides high recognition hit rates.Junta de Castilla y León (Programa de apoyo a proyectos de investigación-Ref. VA036U14)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA013A12-2)Ministerio de Economía, Industria y Competitividad (Grant DPI2014-56500-R