Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.

Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications

Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications

We introduce a literature review considering articles on artificial neural networks in business published in last two decades.412 suitable articles are identified and classified according to defined methodology.We focus on date, area of interest, type of neural network, benchmark method, journal and citations.The most applied are multilayer feedforward networks with backpropagation learning performed by gradient descent algorithm.Majority (25.73%) of the examined articles were published in Expert Systems with Applications. In recent two decades, artificial neural networks have been extensively used in many business applications. Despite the growing number of research papers, only few studies have been presented focusing on the overview of published findings in this important and popular area. Moreover, the majority of these reviews were introduced more than 15 years ago. The aim of this work is to expand the range of earlier surveys and provide a systematic overview of neural network applications in business between 1994 and 2015. We have covered a total of 412 articles and classified them according to the year of publication, application area, type of neural network, learning algorithm, benchmark method, citations and journal. Our investigation revealed that most of the research has aimed at financial distress and bankruptcy problems, stock price forecasting, and decision support, with special attention to classification tasks. Besides conventional multilayer feedforward network with gradient descent backpropagation, various hybrid networks have been developed in order to improve the performance of standard models. Even though neural networks have been established as well-known method in business, there is enormous space for additional research in order to improve their functioning and increase our understanding of this influential area.

Artificial neural networks in business

Several Human-Machine/Computer Interfaces (HMI/HCI) had been developed in recent years. Some designs were specifically made for people with disabilities such as injured-vertebra, apoplexy or poliomyelitis, Amyotrophic Lateral Sclerosis (ALS), and Motor Neuron Disease, (MND). In this paper, we proposed an eye-movement tracking system. Based on Electro-Oculography (EOG) technology we detected the signal with different directions in eye-movements and then analyzed to understand what they represented about (e.g. horizontal direction or vertical direction). We converted the analog signal to digital signal and then used as the control signals for Human-Computer Interface (HCI). In order to make the system ''robust'', several applications with EOG-based HCI had been designed. Our preliminary results revealed more than 90% accuracy rate for examining the eye-movement that may become a new useful human-machine user interface in the near future.

EOG-based Human-Computer Interface system development

Medical data mining by fuzzy modeling with selected features

Automatic text classification based on vector space model (VSM), artificial neural networks (ANN), K-nearest neighbor (KNN), Naives Bayes (NB) and support vector machine (SVM) have been applied on English language documents, and gained popularity among text mining and information retrieval (IR) researchers. This paper proposes the application of VSM and ANN for the classification of Tamil language documents. Tamil is morphologically rich Dravidian classical language. The development of internet led to an exponential increase in the amount of electronic documents not only in English but also other regional languages. The automatic classification of Tamil documents has not been explored in detail so far. In this paper, corpus is used to construct and test the VSM and ANN models. Methods of document representation, assigning weights that reflect the importance of each term are discussed. In a traditional word-matching based categorization system, the most popular document representation is VSM. This method needs a high dimensional space to represent the documents. The ANN classifier requires smaller number of features. The experimental results show that ANN model achieves 93.33% which is better than the performance of VSM which yields 90.33% on Tamil document classification.

Automatic classification of Tamil documents using vector space model and artificial neural network

Face localization, feature extraction, and modeling are the major issues in automatic facial expression recognition. In this paper, a method for facial expression recognition is proposed. A face is located by extracting the head contour points using the motion information. A rectangular bounding box is fitted for the face region using those extracted contour points. Among the facial features, eyes are the most prominent features used for determining the size of a face. Hence eyes are located and the visual features of a face are extracted based on the locations of eyes. The visual features are modeled using support vector machine (SVM) for facial expression recognition. The SVM finds an optimal hyperplane to distinguish different facial expressions with an accuracy of 98.5%.

Facial expression recognition - A real time approach

This study aims to incorporate Artificial Neural Network (ANN) for measuring the effectiveness of the TV broadcast advertisements (toothpaste) by discovering important factors that influence the advertisement effectiveness. The information about the effects of each of these factors has been studied and it is used for measuring the advertisement effectiveness. Fifty attributes are examined to derive values from thirteen factors. These thirteen factors are used as input to ANN model. The data collected from 837 respondents are used for training and testing the ANN. The backpropagation algorithm is used for adjusting the weights in the ANN. Experimental results show that the ANN model achieves 99% accuracy for measuring the advertisement effectiveness.

Measuring advertisement effectiveness—a neural network approach

A neuro-fuzzy model for diagnosis of psychosomatic disorders is proposed in this paper. The symptoms and signs are collected from the patients through oral interview. For the linguistic nature of patient's inputs, an artificial domain is created and fuzzy membership values are defined. The fuzzy values are fed as inputs to feedforward multilayer neural network. The network is trained using Backpropagation training algorithm. The trained model is tested with new patient's symptoms and signs. Further, the performance of the diagnosing capability is compared with medical expert. The performance of the model is also compared with probability model based on Bayesian Belief Network and statistical model using Linear Discriminant analysis

An investigation of neuro-fuzzy systems in psychosomatic disorders

A new neural network model called DIAGNET is proposed in this paper for diagnosing gastrointestinal disorders. DIAGNET is a combination of Backpropagation neural network (BPNN) and radial basis functions neural network (RBFNN). The symptoms and signs are collected from the patients through oral interview. For the linguistic nature of patient’s inputs, an artificial domain is created and fuzzy membership values are defined. The fuzzy values are fed as inputs to the DIAGNET and trained for diagnosing the diseases related to gastrointestinal disorders. The trained model is tested with new patient’s symptoms and signs. The performance of the DIAGNET is compared with the existing Backpropagation neural network and Radial basis functions neural network models. Sensitivity, Specificity and Receiver-Operating Characteristics (ROC) are used as the indicators for testing the accuracy of the models which predict the gastrointestinal disorder diseases. The results suggest that the DIAGNET can be better solution for complex, nonlinear medical decision support systems.

B. Palaniappan

Papers

Automatic classification of Tamil documents using vector space model and artificial neural network

Facial expression recognition - A real time approach

Measuring advertisement effectiveness—a neural network approach

An investigation of neuro-fuzzy systems in psychosomatic disorders

Diagnosis of gastrointestinal disorders using DIAGNET