Raid Saabni^{1, 2} and Alon Schclar¹, ¹The Academic College of Tel-Aviv Yaffo, Israel, ²Traiangle R&D Center, Israel

Automatic Facial Expression Recognition (AFER), has been an active research area in the past three decades. Research and development in this area have become continually active due to its wide range of potential applications in many fields. Recent research in the field presents impressive results when using Convolution Neural Network (CNN's, ConvNets). In general, ConvNets proved to be a very common and promising choice for many computer vision tasks including AFER. Motivated by this fact, we parallelly combine modified versions of three ConvNets to generate an Automated Facial Expression Recognition system. This research aims to present a robust architecture and better learning process for a deep ConvNet. Adding four additional layers to the combination of the basic models assembles the net to one large ConvNet and enables the sophisticated boosting of the basic models. The main contribution of this work comes out of this special architecture and the use of a two-phase training process that enables better learning. The new system we present is trained to detect universal facial expressions of seven\eight basic emotions when targeting the FER2013 and FER2013+ benchmarks, respectively. The presented approach improves the results of the used architectures by 4% using the FER2013 and 2% using FER2013+ data sets. The second round of training the presented system increases the accuracy of some of the basic models by close to 3% while improving the accuracy of the whole net.

Automatic Facial Expression Recognition, Convolutional Neural Networks, Machine Learning, Boosting, Deep Learning.