A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people’s facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism

Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine

Classroom communication involves teacher’s behavior and student’s responses. Extensive research has been done on the analysis of student’s facial expressions, but the impact of instructor’s facial expressions is yet an unexplored area of research. Facial expression recognition has the potential to predict the impact of teacher’s emotions in a classroom environment. Intelligent assessment of instructor behavior during lecture delivery not only might improve the learning environment but also could save time and resources utilized in manual assessment strategies. To address the issue of manual assessment, we propose an instructor’s facial expression recognition approach within a classroom using a feedforward learning model. First, the face is detected from the acquired lecture videos and key frames are selected, discarding all the redundant frames for effective high-level feature extraction. Then, deep features are extracted using multiple convolution neural networks along with parameter tuning which are then fed to a classifier. For fast learning and good generalization of the algorithm, a regularized extreme learning machine (RELM) classifier is employed which classifies five different expressions of the instructor within the classroom. Experiments are conducted on a newly created instructor’s facial expression dataset in classroom environments plus three benchmark facial datasets, i.e., Cohn–Kanade, the Japanese Female Facial Expression (JAFFE) dataset, and the Facial Expression Recognition 2013 (FER2013) dataset. Furthermore, the proposed method is compared with state-of-the-art techniques, traditional classifiers, and convolutional neural models. Experimentation results indicate significant performance gain on parameters such as accuracy, F1-score, and recall

FMX (EEPIS FACIAL EXPRESSION MECHANISM EXPERIMENT): PENGENALAN EKSPRESI WAJAH MENGGUNAKAN NEURAL NETWORK BACKPROPAGATION

In the near future, it is expected that the robot can interact with humans. Communication itself has many varieties. Not only from word to word, but body language also be the medium. One of them is using facial expressions. Facial expression in human communication is always used to show human emotions. Whether it is happy, sad, angry, shocked, disappointed, or even relaxed? This final project focused on how to make robots that only consist of head, so it could make a variety facial expression like human beings. This Face Humanoid Robot divided into several subsystems. There are image processing subsystem, hardware subsystem and subsystem of controllers. In image processing subsystem, webcam is used for image data acquisition processed by a computer. This process needs Microsoft Visual C compiler for programming that has been installed with the functions of the Open Source Computer Vision Library (OpenCV). Image processing subsystem is used for recognizing human facial expressions. With image processing, it can be seen the pattern of an object. Backpropagation Neural Network is useful to recognize the object pattern. Subsystem hardware is a Humanoid Robot Face. Subsystem controller is a single microcontroller ATMega128 and a camera that can capture images at a distance of 50 to 120 cm. The process of running the robot is as follows. Images captured by a camera webcam. From the images that have been processed with image processing by a computer, human facial expression is obtained. Data results are sent to the subsystem controller via serial communications. Microcontroller subsystem hardware then ordered to make that facial expression. Result of this final project is all of the subsystems can be integrated to make the robot that can respond the form of human expression. The method used is simple but looks quite capable of recognizing human facial expression. Keyword: OpenCV, Neural Network BackPropagation, Humanoid Robo

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

Time-Efficient Hybrid Approach for Facial Expression Recognition

Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database