A Real Time Facial Expression Classification System Using Local Binary Patterns (original) (raw)

Facial Expression Recognition System Using Haar Cascades Classifier

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Facial expression conveys non-verbal cues, which play a crucial role in social relations. Facial Expression Recognition is a significant yet challenging task, as we can use it to identify the emotions and the mental state of an individual. In this system, using image processing and machine learning, we compare the captured image with the trained dataset and then display the emotional state of the image. To design a robust facial feature recognition system, Local Binary Pattern(LBP) is used. We then assess the performance of the suggested system by using a database that is trained, with the help of Neural Networks. The results show the competitive classification accuracy of various emotions.

Facial Expression Recognition System: A Practical Implementation

2012

Facial expression is one of the most powerful and immediate means for human beings to communicate their emotions, intentions, and opinions to each other. Facial expressions also provide information about cognitive state, such as interest, boredom, confusion, and stress. Facial expressions are natural and can express emotions sooner than people verbalize their feelings. It conveys non-verbal cues, which play an important role in interpersonal relations. Facial expressions recognition technology helps in designing intelligent human computer interfaces. In this paper facial expression recognition technique has been performed on the Indian faces extracted from a video. Initially, a live video of Indian college students is given as input to Haar classifier which traces out the faces from it. Then 42 facial feature points are detected using Active Appearance Model (AAM) technique using which we extract the facial features that are to be mapped on the extracted faces. In the last step four...

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.

Results: Recognition of Facial Expression by Digital Image Processing

— Facial expression is one of most important behavioral measure for studies of emotion, cognitive processes, and social interaction. Facial expression recognition has become a promising research area. Its applications in many areas like human-computer interfaces, human emotion analysis, and medical care and cure. Automatic facial expression recognition is an interesting and challenging subject in digital signal processing, pattern recognition, artificial intelligence, etc. In this paper use a new method of facial expression recognition based on local binary patterns (LBP). The LBP features are firstly extracted from the original images of facial expression then face area is divided into small parts from which Local Binary Pattern (LBP) histograms are extracted into a single, spatially enhanced feature histogram efficiently representing the face image. Keywords— local binary pattern (LBP), feature extraction, distribution, pattern recognition, histogram, feature vector.

Using support vector machine and local binary pattern for facial expression recognition

Facial expressions are natural ways by which people can express their feelings and emotions. In the field of affective computing and human computer interactions, a better result will be achieved when we have an intelligent interface(s) that could act and behaves in a way similar to that of human being. This research intends to bring about the development of a face recognition model and applying it to a real-data set of expressions. Five expressions will be classified which include: fear, happiness, disgust, sadness and surprise using the innovations of support vector machine (SVM) and local binary pattern (LBP). The students of Federal University of Technology, Akure (FUTA) will be used as a case study. LBP will be used for feature extraction while SVM will be used for classification and recognition of expressions

A REVIEW ON AUTOMATIC FACIAL EXPRESSION RECOGNITION SYSTEMS

The automatic facial expression recognition (FER) system is an important concept in human computer interface (HCI), as human face is a part that gives information about the state of user's behavior through various expressions. This study of recognizing facial expression is one of the challenging research areas in image analysis and computer vision. Since last 2 decades many researchers are working to make HCI machines to operate with more reliability and efficiency even in the worst conditions. In this paper, we studied different FER methods such as face detection, feature extraction and expression classification where techniques like Knowledge-based, Feature-based, Principal Component Analysis (PCA), Independent Component Analysis (ICA), Gabor filters, Local Binary Patterns (LBP) with a range of classifiers like a SVM, Adaboost, HMM etc are been compared.

A Facial Expression Recognition System A Project Report

Facial Expression conveys non-verbal cues, which plays important roles in interpersonal relations. The Facial Expression Recognition system is the process of identifying the emotional state of a person. In this system captured image is compared with the trained dataset available in database and then emotional state of the image will be displayed. This system is based on image processing and machine learning. For designing a robust facial feature descriptor, we apply the Local Binary Pattern. Local Binary Pattern (LBP) is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. The histogram will be formed by using the operator label of LBP. The recognition performance of the proposed method will be evaluated by using the trained database with the help of Support Vector Machine. Experimental results with prototypic expressions show the superiority of the LBP descriptor against some well-known appearance-based feature representation methods. We evaluate our proposed method on the JAFFE and COHN-KANADE dataset. The Precision, Recall and Fscore from the COHN-KANADE dataset were 83.6142%, 95.0822% and 88.9955% respectively and that of JAFFE dataset were 91.8986%, 98.3649%, 95.0218% respectively. Experimental results demonstrate the competitive classification accuracy of our proposed method.

Emotion Recognition Using Real Time Face Recognition

— Facial expressions are the fastest means of communication while conveying any type of information. These are not only exposes the sensitivity or feelings of any person but can also be used to judge his/her mental views. Facial detection in images is the foremost step towards facial recognition and expression recognition along with face localization. High degree of variability in the images that can be obtained of faces due to varying conditions of lighting, exposure, color and expression. Using Machine learning tools and algorithms such as OpenCV 3.4.0 and the Haar Cascade Classifier. This research paper details our approach towards creating a semi-automated with a slight degree of human program which can be used to simultaneously detect multiple users and provide an effective solution to facial recognition using minimal amount of resources.