This article proposes a novel framework for the recognition of six universal facial expressions. The framework is based on three sets of features extracted from a face image: entropy, brightness, and local binary pattern. First, saliency maps are obtained using the state-of-the-art saliency detection algorithm “frequency-tuned salient region detection”. The idea is to use saliency maps to determine appropriate weights or values for the extracted features (i.e., brightness and entropy). We have performed a visual experiment to validate the performance of the saliency detection algorithm against the human visual system. Eye movements of 15 subjects were recorded using an eye-tracker in free-viewing conditions while they watched a collection of 54 videos selected from the Cohn-Kanade facial expression database. The results of the visual experiment demonstrated that the obtained saliency maps are consistent with the data on human fixations. Finally, the performance of the proposed framework is demonstrated via satisfactory classification results achieved with the Cohn-Kanade database, FG-NET FEED database, and Dartmouth database of children’s faces.

/pdf/saliency-based-framework-for-facial-expression-recognition-4udtnsu1xm.pdf

Saliency-based framework for facial expression recognition

Human emotion recognition has become one of the key steps towards advanced human-machine interactions Brain waves or Electroencephalography (EEG) is one of the frequently used bio signals in emotion detection as it is found that the signal measured from the central nervous system has a relationship between physiological changes and emotions Using facial expressions is another mode that could be used for emotion recognition using external physiological signals This project investigates the possibility of identifying emotions using brain signals and facial expressions EEG feature extraction is done, using Relative Wavelet Energy calculation and Discrete Wavelet Transform methods for feature extraction, and Artificial Neural Network for emotion classification For facial feature extraction Active Shape Model is used while the facial emotion classification is done using a Support Vector Machine The solution could be used to study about the behaviour of EEG signals as well as facial expressions in different mental states

Intelligent emotion recognition system using electroencephalography and active shape models

Emotion recognition systems using different modalities have become an emerging area of research over the last two decades. This chapter mainly focuses on a review of emotion recognition using modalities like face, speech, gesture, text, and electroencephalogram signals, and gives descriptions of available databases. It provides the basic knowledge to understand emotion recognition systems with different modalities and relevant features.

Chapter 5 – Emotion Recognition

A major issue which divides the facial expressions from the other classification domains is complicated behaviour of human to express the emotions which should be recognised with the classifier model. Existing research recognise the emotions using a range of classification techniques. However, low accuracy rate, large training set, large extracted features or priority for sequence images are the main drawbacks of those works. One of the recent techniques to address the facial expressions problem is fuzzy rule-based system FRBS which is used as a successful method to model and solve the natural-based problems. However, FRBS is poor to adapt the existing knowledge with the diverse conditions. In this article a novel hybrid genetic-fuzzy rule-based model is proposed to optimise the performance of fuzzy classification while the limited raw input data as the features are used. In this model, the proposed genetic algorithm simulates the honey bees offspring generation process called bee royalty offspring algorithm BROA to improve the training process of classic genetic algorithm. The comparison results illustrated that the genetic-fuzzy classification model improves considerably the accuracy rate and performance of FRBS while the BROA modify the training process of genetic-based algorithms.

Bee royalty offspring algorithm for improvement of facial expressions classification model

This paper discusses a deep learning-based image processing method to recognize human emotion from their facial expression with special concentration on infant’s face between one to five years of age. The work has importance because most of the time it becomes necessary to understand need of a child from their facial expression and behavior. This work is still a challenge in the field of Human Facial Emotion Recognition due to confusing facial expression that sometimes found in some of the samples. We have tried to recognize any facial expression into one of the mostly understood human mood namely Angry, Disgust, Fear, Happy, Sad, Surprise and Neutral. For this purpose, we have trained an image classifier with Convolutional Neural Network with Kaggle's Fer2013 Dataset. After the completion of the project, we achieved good accuracy in most of the prominent emotions by testing with 20 random images for each emotion.

/pdf/human-emotion-recognition-using-deep-learning-with-special-1ankrudo.pdf

Human Emotion Recognition using Deep Learning with Special Emphasis on Infant’s Face

This thesis describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of a problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the requirements of such system are specified. The FER system consists of 3 stages: face detection, feature extraction and expression recognition. Methods proposed in literature are reviewed for each stage of a system. Finally, the design and implementation of my system are explained. The face detection algorithm used in the system is based on work by Viola and Jones [13]. The expressions are described by appearance features obtained from texture encoded with Local Binary Patterns [32]. The Support Vector Machine with RBF kernel function is used for classification. Databases that were used are: The Facial Expressions and Emotion Database [34], which contains spontaneous emotions and CohnKanade Database [35] with posed emotions. The system was trained on two databases separately and achieves accuracy of 71% for spontaneous emotions recognition and 77% for posed actions recognition.

/pdf/facial-expression-recognition-system-26p4zgsy4i.pdf

Facial Expression Recognition System

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Facial Expression Recognition System