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.

eXnet: An Efficient Approach for Emotion Recognition in the Wild Facial expression recognition has been well studied for its great importance in the areas of humanxe2x80x93computer interaction and social sciences. With the evolution of deep learning, there have been significant advances in this area that also surpass humanlevel accuracy. Although these methods have achieved good accuracy, they are still suffering from two constraints (high computational power and memory), which are incredibly critical for small hardwareconstrained devices. To alleviate this issue, we propose a new Convolutional Neural Network (CNN) architecture eXnet (Expression Net) based on parallel feature extraction which surpasses current methods in accuracy and contains a much smaller number of parameters (eXnet: 4.57 million, VGG19: 14.72 million), making it more efficient and lightweight for realtime systems. Several modern data augmentation techniques are applied for generalization of eXnet; these techniques improve the accuracy of the network by overcoming the problem of overfitting while containing the same size. We provide an extensive evaluation of our network against key methods on Facial Expression Recognition 2013 (FER2013), Extended CohnKanade Dataset (CK), and Realworld Affective Faces Database (RAFDB) benchmark datasets. We also perform ablation evaluation to show the importance of different components of our architecture. To evaluate the efficiency of eXnet on embedded systems, we deploy it on Raspberry Pi 4B. All these evaluations show the superiority of eXnet for emotion recognition in the wild in terms of accuracy, the number of parameters, and size on disk.