Fully Automatic Coding of Basic Expressions from Video (original) (raw)
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International Journal of Advance Research and Innovative Ideas in Education, 2019
Facial Expressions are considered as one of the channels that convey human emotions. The task of emotion recognition often involves the analysis of human expressions in multi-modal forms such as images, text, audio or video. Different emotion types are identified through the integration of features from facial expressions. This information contains particular feature points that are used to analyse expressions or emotions of the person. These feature points are extracted using image processing techniques. The proposed system focuses on categorizing the set of 68 feature points into one of the six universal emotions i.e. Happy, Sad, Anger, Disgust, Surprise and Fear. For collecting these points, a series of images is given as input to the system. Feature points are extracted and corresponding co-ordinates of the points are obtained. Based on the distances co-ordinates from centroid, images are classified into one of the universal emotions. Existing system show recognition accuracy mo...
Project Unit: HCI(Human Computer Interaction) Project name: Facial Expression Recognition System
The problem of automatic recognition of facial expressions is still an ongoing research, and it relies on advancements in Image Processing and Computer Vision techniques. Such systems have a variety of interesting applications, from human-computer interaction, to robotics and computer animations. Their aim is to provide robustness and high accuracy, but also to cope with variability in the environment and adapt to real time scenarios. This paper proposes an automatic facial expression recognition system, capable of distinguishing the six universal emotions: disgust, anger, fear, happiness, sadness and surprise. It is designed to be person independent and tailored only for static images. The system integrates a face detection mechanism using Viola-Jones algorithm, uses uniform Local Binary Patterns for feature extraction and performs classification using a multi-class Support Vector Machine model.
MACHINE LEARNING TECHNIQUES FOR DETECTING AND RECOGNISING EMOTIONS BY FACIAL EXPRESSIONS
2023
Non-verbal communication, such as facial expression, also includes the use of body language and vocal inflection to communicate emotion. Facial expressions may be used to many different purposes. Computer science, Biotechnology, Psychology, Chemistry, and Pharmacy all get additional interest as a result of face expression recognition. Expressions used in HCI studies to better understand human-computer interactions. Facial expression identification paves the way for precise extraction of emotional characteristics. Static picture facial expression identification methods neglect the dynamic and static properties of facial organs and muscle movements, as well as the geometry and visual elements of facial emotions. By performing patch matching procedures and choosing critical patches, we are able to overcome this constraint utilising extracted 3D Gabor features based on patches. Positive findings from the testing phase include an increased Correct Recognition Rate (CRR), better performance when taking facial characteristics and bodily movements into account, fewer incorrect face registrations, and shorter processing time. The suggested method provided the greatest CRR on the JAFFE and Cohn-Kanade AU-Coded Facial Expression datasets, establishing a clear advantage above state-of-the-art methods.
Review on Emotion Recognition Using Facial Expressions
European Journal of Electrical Engineering and Computer Science
The advent of artificial intelligence technology has reduced the gap between humans and machines as equips man to create more near-perfect humanoids. Facial expression is an important tool to communicate one’s emotions as a non-verbally overview of emotion recognition using facial expressions. A remarkable advantage of such a technique recently improved public security through tracking and recognizing, thus led to the high attention to keep up the scientific research in the field. The approaches used for facial expression include classifiers like Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolution Neural Network (CNN), Active Appearance and Machine learning which all used to classify emotions based on certain parts of interest on the face like lips, lower jaw, eyebrows, cheeks and many more. By comparison, the reviews have shown that the average accuracy of the basic emotion ranged from 51% up to 100%, whereas carrying through 7% to 13% in the compound emotion...