Human Emotion Recognition by Using Pattern Recognition Network (original) (raw)
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Human Emotion Recognition System
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IJERT-Human Emotion Recognition using Neural Network Technique.
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https://www.ijert.org/human-emotion-recognition-using-neural-network-technique https://www.ijert.org/research/human-emotion-recognition-using-neural-network-technique-IJERTV5IS040779.pdf Emotions play an important role in human to human interaction to have smoother communication. By using emotion recognition systems human computer interaction can also be improved.Due to increasing demand and requirement of improvement in human computer interface in recent years, emotion recognition systems are developed.In this proposed work image database is collected and seven basic emotions are classified using neural network as classifier.
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Facial emotion plays a vital role for human interactive communication and also used in numerous real applications. Facial expression identification from frontal still images has in recent times become a hopeful investigation area. Their applications include human-computer interface, human emotion examination robot control, driver state surveillance and medical fields. This paper aims to develop emotion classification scheme to identify seven dissimilar facial emotions, such as surprise, sad, neutral, happy, fear, disgust and anger by using JAFFE database. Two different approaches of feature selection and extraction have been used for generation of optimal feature vector. LBP and 2D-DCT coefficients are employed in addition to image statistics, texture and entropy parameters. In order to reduce the high dimensionality of the inputs, the principal component analysis has been used and significant reduction in the input-dimensionality has been achieved. The single hidden layer feed forward neural network has been used as a classifier in order to classify different emotions from frontal facial images. Three learning algorithms such as resilient backpropagation, scaled conjugate gradient and gradient descent algorithm with momentum and adaptive learning rate have been compared. It has been observed that our meticulously designed LBP based hybrid feature vector and a single hidden layer neural network containing only 70 neurons in the hidden layer trained with gradient descent algorithm with momentum and adaptive learning rate delivers the maximum average overall classification accuracy of 97.2%, which has not been reported so far in the literature for the said database. The proposed neural network is very compact, as it is comprised of only 5,607 connection weights including biases.
Human Face Detection and Emotion Recognition using Neural Network
A Journal of TUTA, Paschimanchal campus, 2018
Automatic facial emotion recognition is the active research area and challenging task in computer vision. Computers are used to gain high level understanding from digital images and videos. Automatic face detection and emotion recognition plays important role in human machine interaction. Tradition approaches of machine learning requires complex feature extraction process and produced poor results. In this paper, Convolution neural network of deep learning is proposed to exactly and accurately interpret information available in human faces. Basic emotions of human faces such as happy, sad, disgusting, fear, angry and disgusting will be evaluated. The neural network architecture is trained with large dataset and tested to obtain the best results with high accuracy.
Behaviors, actions, poses, facial expressions and speech; these are considered as channels that convey human emotions. Extensive research has being carried out to explore the relationships between these channels and emotions. This paper proposes a prototype system which automatically recognizes the emotion represented on a face. Thus a neural network based solution combined with image processing is used in classifying the universal emotions: Happiness, Sadness, Anger, Disgust, Surprise and Fear. Colored frontal face images are given as input to the prototype system. After the face is detected, image processing based feature point extraction method is used to extract a set of selected feature points. Finally, a set of values obtained after processing those extracted feature points are given as input to the neural network to recognize the emotion contained.
Detection and Recognition of Human Emotion using Machine Learning
International Journal of Scientific Research in Science and Technology, 2023
This paper describes an emotion detection system based on real-time detection using image processing with human-friendly machine interaction. Facial detection has been around for decades. Taking a step ahead, human expressions displayed by face and felt by the brain, captured via video, electric signal, or image form can be approximated. To recognize emotions via images or videos is a difficult task for the human eye and challenging for machines thus detection of emotion by a machine requires many image processing techniques for feature extraction. This paper proposes a system that has two main processes such as face detection and facial expression recognition (FER). This research focuses on an experimental study on identifying facial emotions. The flow for an emotion detection system includes the image acquisition, preprocessing of an image, face detection, feature extraction, and classification. To identify such emotions, the emotion detection system uses KNN Classifier for image classification, and Haar cascade algorithm an Object Detection Algorithm to identify faces in an image or a real-time video. This system works by taking live images from the webcam. The objective of this research is to produce an automatic facial emotion detection system to identify different emotions based on these experiments the system could identify several people that are sad, surprised, and happy, in fear, are angry, etc.
Human Emotion Detection Using Image Processing
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In todays world of technology human cannot survive without being techno-freak. Just to get workplace environment friendly we are going to introduce six emotions and positive and negative emotion recognition methods using facial image and the the development of app based on the method. In this project we will use the Deep Learning technology to generate models with emotion based facial expressions to recognized emotions. Inevitebly feelings play an important role not only in our relations with other people but also in the way we use Computers. Affective computing is a domain that focuses on user emotions while he interacts with computers and applications. As emotional state of person may influence concentration, task solving and decision making skills, effective computing vision is to make system stable to recognize and influence human emotions in order to enhance productivity and effectiveness of working with computers. We will develop an automated system to recognize six emotions a...
Paper contains emotion recognition system based on facial expression using Geometric approach. A human emotion recognition system consists of three steps: face detection, facial feature extraction and facial expression classification. In this paper, we used an anthropometric model to detect facial feature points. The detected feature points are group into two class static points and dynamic points. The distance between static points and dynamic points is used as a feature vector. Distance changes as we track these points in image sequence from neutral state to corresponding emotion. These distance vectors are used for input to classifier. SVM (Support Vector Machine) and RBFNN (Radial Basis Function Neural Network) used as classifier. Experimental results shows that the proposed approach is an effective method to recognize human emotions through facial expression with an emotion average recognition rate 91 % for experiment purpose the Cohn Kanade databases is used.