Discrete choice models for static facial expression recognition In this paper we propose the use of Discrete Choice Analysis (DCA) for static facial expression classification. Facial expressions are described with expression descriptive units (EDU), consisting in a set of high level features derived from an active appearance model (AAM). The discrete choice model (DCM) is built considering the 6 universal facial expressions plus the neutral one as the set of the available alternatives. Each alternative is described by an utility function, defined as the sum of a linear combination of EDUs and a random term capturing the uncertainty. The utilities provide a measure of likelihood for a combinations of EDUs to represent a certain facial expression. They represent a natural way for the modeler to formalize her prior knowledge on the process. The model parameters are learned through maximum likelihood estimation and classification is performed assigning each test sample to the alternative showing the maximum utility. We compare the performance of the DCM classifier against Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (GDA), Relevant Component Analysis (RCA) and Support Vector Machine (SVM). Quantitative preliminary results are reported, showing good and encouraging performance of the DCM approach both in terms of recognition rate and discriminatory power.

Facial Expression Recognition Using DCNN and Development of an iOS App for Children with ASD to Enhance Communication Abilities In this paper, continued work of a research project is discussed which achieved the end goal of the project  to build a mobile device application that can teach children with Autism Spectrum Disorder (ASD) to recognize human facial expressions utilizing computer vision and image processing. Universally, there are seven facial expressions categories: angry, disgust, happy, sad, fear, surprise, and neutral. To recognize all these facial expressions and to predict the current mood of a person is a difficult task for a child. A child with ASD, this problem presents itself in a more sophisticated manner due to the nature of the disorder. The main goal of this research was to develop a deep Convolutional Neural Network (DCNN) for facial expression recognition, which can help young children with ASD to recognize facial expressions, using mobile devices. The Kaggleu0027s FER2013 and Karolinska Directed Emotional Faces (KDEF) dataset have been used to train and test with the DCNN model, which can classify facial expressions from different viewpoints and in different lighting contrasts. An 86.44% accuracy was achieved with good generalizability for the DCNN model. The results show an improvement of the DCNN accuracy in dealing with lighting contrast changes, and the implementation of image processing before performing the facial expression classification. As a byproduct of this research project, an app suitable for the iOS platform was developed for running both the DCNN model and image processing algorithm. The app can be used by speechlanguage pathologies, teacher, caretakers, and parents as a technological tool when working with children with ASD.