Emotion Recognition using Image Processing

2021

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...

Humans can use vision to identify objects quickly and accurately. Computer Vision seeks to emulate human vision by analyzing digital image inputs. For humans to detect an emotion will not be a difficult job to perform as humans are linked with emotions themselves but for a computer detecting an emotion will be difficult job to perform. Detecting emotion through voice, for example: detecting 'stress' in a voice by setting parameters in areas like tone, pitch, pace, volume etc can be achieved but in case of digital images detecting emotion just by analyzing images is a novel way.

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.

International Journal of Advanced Research in Computer Science

The field of image processing and analysis provides solution for many complex problems such as enhancement of degraded images for the purpose of clarity, medical image processing, biometric identification etc. Automatic emotion detection form the facial image is also comes under these categories of complex issues. The main challenges in this area are: different color complexion of persons over different regions of the globe, facial accessories, pose variations etc. This paper shows the overall process of automatic recognition of emotions and highlight key issues or challenges in this fields. At the end a system is proposed to overcome these issues.

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.

ArXiv, 2020

In this work, user's emotion using its facial expressions will be detected. These expressions can be derived from the live feed via system's camera or any pre-exisiting image available in the memory. Emotions possessed by humans can be recognized and has a vast scope of study in the computer vision industry upon which several researches have already been done. The work has been implemented using Python (2.7, Open Source Computer Vision Library (OpenCV) and NumPy. The scanned image(testing dataset) is being compared to the training dataset and thus emotion is predicted. The objective of this paper is to develop a system which can analyze the image and predict the expression of the person. The study proves that this procedure is workable and produces valid results.

Human can see emotion as associated with mood, temperament, personality and disposition. To detect emotions is easy for humans but it's quite difficult for computer as world is three dimensional but Computer has only two dimensions. Computer seeks to emulate the human emotions by digital image analysis. Humans can use vision to identify objects quickly and accurately. Human can detect emotion using voice based on different parameters like tone, pitch, pace and volume; but in case of digital images detecting emotion just by analysing images is a novel way. This algorithm has two major parts. First, Template database generation and another is emotion detection. In this, we first extracts face from an image using some basic image processing operations and color models in it. Here we define thresholds to separate our region of interest. Then we perform lip detection on cropped face and this extracted lips are stored in our database with emotion name in database generation phase while in emotion detection phase this extracted lips are compared with series of stored template in database and on the basis of best correlated template emotion is recognized. This method of detecting emotions is simple and fast as compared to previous methods i.e. using brain activity or by speech. Size of database will affect the effectiveness of the proposed algorithm.

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.

International Journal of Innovative Research in Computer Science & Technology

Expressions and body language can tell us a lot about what people are thinking. They are a form of non-verbal communication which tells us about how the person is feeling. It describes the mood of the person like whether he is happy or sad. This detection can be done using various techniques which are already based in the research papers like instrumented sensor technology and computer vision. It means that the expressions can be classified under different techniques like whether motion of the person is still or he is moving. This paper focuses on detecting the emotions of the person using computer vision. Using the Artificial Intelligence Technique and Mediapipe along with Computer Vision we are focusing on various joints in our body and storing their coordinates in a python file created there and then testing our Algorithm to detect the mood of the person. In addition, a dialogue box also pops us while detecting the emotions which tells us about the probability i.e the accuracy of...

Identifying Human Emotion is important in facilitating communication and interactions between individuals. They are also used as an important mean in studying behavioral Science and in studying Psychological changes. Since face is the prime source for recognizing human emotion, the proposed system will provide a quick and practical approach for non-invasive emotion detection. The recognition of emotions is done by deploying an intelligent system using neural network, signal processing and image processing toolbox of Matlab 7.12.0(R2011a). The network classifier is pattern recognition network which is actually a feed forward neural network that will be trained for some images bearing different emotions. The trained network is then simulated to test the new data for recognizing different emotions.