A Study of Three Artificial Neural Networks Models\u27 Ability to Identify Emotions from Facial Images

Facial expressions conveying emotions are vital for human communication. They are also important in the studies of human interaction and behavioral studies. Recognition of emotions, using facial images, may provide a fast and practical approach that is noninvasive. Most previous studies of emotion recognition through facial images were based on the Facial Action Coding System (FACS). The FACS, which was developed by Ekman and Freisen in 1978, was created to identify different facial muscular actions. Previous artificial neural network-based approaches for classification of facial expressions focused on improving one particular neural network model for better accuracy. The purpose of this present study was to compare different artificial neural network models, and determine which model was best at recognizing emotions through facial images. The three neural network models were: 1 . The Hopfield network 2. The Learning Vector Quantization network 3. Multilayer (Feedforward) back-propagation network Several facial parameters were extracted from facial images and used in training the different neural network models. The best performing neural network was the Hopfield network at 72.50% accuracy. Next, the facial parameters were tested for their significance in identifying facial expressions and a subset of the original facial parameters was used to retrain the networks. The best performing network using the subset of facial parameters was the LVQ network at 67.50% accuracy. This study has helped to understand which neural network model was best at identifying facial expression and to understand the importance of having a good set of parameters representing the facial expression. This study has shown that more research is needed to find a good set of parameters that will improve the accuracy of emotion identification using artificial neural networks

Hierarchical Associative Memory Based on Oscillatory Neural Network

In this thesis we explore algorithms and develop architectures based on emerging nano-device technologies for cognitive computing tasks such as recognition, classification, and vision. In particular we focus on pattern matching in high dimensional vector spaces to address the nearest neighbor search problem. Recent progress in nanotechnology provides us novel nano-devices with special nonlinear response characteristics that fit cognitive tasks better than general purpose computing. We build an associative memory (AM) by weakly coupling nano-oscillators as an oscillatory neural network and design a hierarchical tree structure to organize groups of AM units. For hierarchical recognition, we first examine an architecture where image patterns are partitioned into different receptive fields and processed by individual AM units in lower levels, and then abstracted using sparse coding techniques for recognition at higher levels. A second tree structure model is developed as a more scalable AM architecture for large data sets. In this model, patterns are classified by hierarchical k-means clustering and organized in hierarchical clusters. Then the recognition process is done by comparison between the input patterns and centroids identified in the clustering process. The tree is explored in a "depth-only" manner until the closest image pattern is output. We also extend this search technique to incorporate a branch-and-bound algorithm. The models and corresponding algorithms are tested on two standard face recognition data-sets. We show that the depth-only hierarchical model is very data-set dependent and performs with 97% or 67% recognition when compared to a single large associative memory, while the branch and bound search increases time by only a factor of two compared to the depth-only search

Explainable Hopfield Neural Networks Using an Automatic Video-Generation System

Hopfield Neural Networks (HNNs) are recurrent neural networks used to implement associative memory. They can be applied to pattern recognition, optimization, or image segmentation. However, sometimes it is not easy to provide the users with good explanations about the results obtained with them due to mainly the large number of changes in the state of neurons (and their weights) produced during a problem of machine learning. There are currently limited techniques to visualize, verbalize, or abstract HNNs. This paper outlines how we can construct automatic video-generation systems to explain its execution. This work constitutes a novel approach to obtain explainable artificial intelligence systems in general and HNNs in particular building on the theory of data-to-text systems and software visualization approaches. We present a complete methodology to build these kinds of systems. Software architecture is also designed, implemented, and tested. Technical details about the implementation are also detailed and explained. We apply our approach to creating a complete explainer video about the execution of HNNs on a small recognition problem. Finally, several aspects of the videos generated are evaluated (quality, content, motivation and design/presentation).University of the Bio-Bio. Vicerrectoria de Investigacion. Facultad de Ciencias Empresariales. Departamento de Sistemas de Informacion

Emotional State Recognition Based on Physiological Signals

Emotsionaalsete seisundite tuvastamine on väga tähtis inimese ja arvuti vahelise suhtlemise (Human-Computer Interaction, HCI) jaoks. Tänapäeval leiavad masinõppe meetodid ühe enam rakendust paljudes inimtegevuse valdkondades. Viimased uuringud näitavad, et füsioloogiliste signaalide analüüs masinõppe meetoditega võiks võimaldada inimese emotsionaalse seisundi tuvastamist hea täpsusega. Vaadates emotsionaalse sisuga videosid, või kuulates helisid, tekib inimesel spetsifiline füsiloogiline vastus. Antud uuringus me kasutame masinõpet ja heuristilist lähenemist, et tuvastada emotsionaalseid seisundeid füsioloogiliste signaalide põhjal. Meetodite võrdlus näitas, et kõrgeim täpsus saavutati juhuslike metsade (Random Forest) meetodiga rakendades seda EEG signaalile, mis teisendati sagedusintervallideks. Ka kombineerides EEG-d teiste füsioloogiliste signaalidega oli tuvastamise täpsus suhteliselt kõrge. Samas heuristilised meetodid ja EEG signaali klassifitseerimise rekurrentse närvivõrkude abil ebaõnnestusid. Andmeallikaks oli MAHNOB-HCI mitmemodaalne andmestik, mis koosneb 27 isikult kogutud füsioloogilistest signaalidest, kus igaüks neist vaatas 20 emotsionaalset videolõiku. Ootamatu tulemusena saime teada, et klassikaline Eckman'i emotsionaalsete seisundite nimekiri oli parem emotsioonide kirjeldamiseks ja klassifitseerimiseks kui kaasaegne mudel, mis esitab emotsioone valentsuse ja ärrituse teljestikul. Meie töö näitab, et emotsiooni märgistamise meetod on väga tähtis hea klassifitseerimismudeli loomiseks, ning et kasutatav andmestik peab sobima masinõppe meetodite jaoks. Saadud tulemused võivad aidata valida õigeid füsioloogilisi signaale ja emotsioonide märkimise meetodeid uue andmestiku loomisel ja töötlemisel.Emotional state recognition is a crucial task for achieving a new level of Human-Computer Interaction (HCI). Machine Learning applications penetrate more and more spheres of everyday life. Recent studies are showing promising results in analyzing physiological signals (EEG, ECG, GSR) using Machine Learning for accessing emotional state. Commonly, specific emotion is invoked by playing affective videos or sounds. However, there is no canonical way for emotional state interpretation. In this study, we classified affective physiological signals with labels obtained from two emotional state estimation approaches using machine learning algorithms and heuristic formulas. Comparison of the method has shown that the highest accuracy was achieved using Random Forest classifier on spectral features from the EEG records, a combination of features for the peripheral physiological signal also shown relatively high classification performance. However, heuristic formulas and novel approach for ECG signal classification using recurrent neural network ultimately failed. Data was taken from the MAHNOB-HCI dataset which is a multimodal database collected on 27 subjects by showing 20 emotional movie fragment`s. We obtained an unexpected result, that description of emotional states using discrete Eckman's paradigm provides better classification results comparing to the contemporary dimensional model which represents emotions by matching them onto the Cartesian plane with valence and arousal axis. Our study shows the importance of label selection in emotion recognition task. Moreover, obtained dataset have to be suitable for Machine Learning algorithms. Acquired results may help to select proper physiological signals and emotional labels for further dataset creation and post-processing

Automatic recognition of Bangla sign language

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2012.Cataloged from PDF version of thesis report.Includes bibliographical references (page 39).Sign Language is the mode of communication among the deaf and dumb. However, integrating them into the main stream is very difficult as the majority of the society is unaware of their language. So, to bridge the communication gap between the hearing and speech impaired and the rest in Bangladesh, we conducted a research to recognize Bangla sign language using a computer-vision based approach. To achieve our goals we used Neural Networks to train individual signs. In the future, this research, besides helping as an interpreter, can also open doors to numerous other applications like sign language tutorials or dictionaries and also help the deaf and dumb to search the web or send mails more conveniently.Najeefa Nikhat ChoudhuryGolam KayasB. Computer Science and Engineerin