Stackingbased deep neural network for Facial Expression Recognition We present a scalable stackingbased deep neural network(SDNN) for facial expression recognition. The network is a congregate of basic learning models in series to synthesize a deep neural network with feedforward network architecture. Thur, choosing trainable learning modules is the core to effectively build SDNN in an endtoend manner. Inspired by the manifold learning archetype, we implement a Patch Discriminative Analysis(PDA) as a basic learning model, followed by hashing and block histogram on the top, which sample image in a low discriminative space, and finding an efficient representation of the training data. As those selflearnable models trained, a low dimensional discriminative feature is implicitly learned, which proves to be useful in facial expression recognition. Experimental results on the facial expression dataset(CK) show that the proposed model is superior to its counterparts, capable of achieving stateoftheart performance.

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.