IMPROVING CNN FEATURES FOR FACIAL EXPRESSION RECOGNITION

Abstract Facial expression recognition is one of the challenging tasks in computervision. In this paper, we analyzed and improved the performances bothhandcrafted features and deep features extracted by Convolutional NeuralNetwork (CNN). Eigenfaces, HOG, Dense-SIFT were used as handcrafted features.Additionally, we developed features based on the distances between faciallandmarks and SIFT descriptors around the centroids of the facial landmarks,leading to a better performance than Dense-SIFT. We achieved 68.34 % accuracywith a CNN model trained from scratch. By combining CNN features withhandcrafted features, we achieved 69.54 % test accuracy.Key Word: Neural network, facial expression recognition, handcrafted feature

A physiological signal database of children with different special needs for stress recognition

This study presents a new dataset AKTIVES for evaluating the methods for stress detection and game reaction using physiological signals. We collected data from 25 children with obstetric brachial plexus injury, dyslexia, and intellectual disabilities, and typically developed children during game therapy. A wristband was used to record physiological data (blood volume pulse (BVP), electrodermal activity (EDA), and skin temperature (ST)). Furthermore, the facial expressions of children were recorded. Three experts watched the children's videos, and physiological data is labeled "Stress/No Stress" and "Reaction/No Reaction", according to the videos. The technical validation supported high-quality signals and showed consistency between the experts.Scientific and Technological Research Council of Turkey Technology and Innovation Funding Programmes Directorat

Hybrid Deep Neural Network for Facial Expressions Recognition

Facial expressions are critical indicators of human emotions where recognizing facial expressions has captured the attention of many academics, and recognition of expressions in natural situations remains a challenge due to differences in head position, occlusion, and illumination. Several studies have focused on recognizing emotions from frontal images only, while in this paper wild images from the FER2013 dataset have been used to make a more generalizing model with the existence of its challenges, it is among the most difficult datasets that only got 65.5 % accuracy human-level. This paper proposed a model for recognizing facial expressions using pre-trained deep convolutional neural networks and the technique of transfer learning. this hybrid model used a combination of two pre-trained deep convolutional neural networks, training the model in multiple cases for more efficiency to categorize the facial expressions into seven classes. The results show that the best accuracy of the suggested models is 74.39%  for the hybrid model, and 73.33% for Fine-tuned the single EfficientNetB0 model, while the highest accuracy for previous methods was 73.28%. Thus, the hybrid and single models outperform other state of art classification methods without using any additional, the hybrid and single models ranked in the first and second position among these methods. Also, The hybrid model has even outperformed the second-highest in accuracy method which used extra data. The incorrectly labeled images in the dataset unfairly reduce accuracy but our best model recognized their actual classes correctly