scholarly journals FACIAL EMOTION DETECTION USING OPEN CV

2022 ◽  
Vol 06 (01) ◽  
Author(s):  
Yashwanth D
Keyword(s):  
Face Detection ◽  
Facial Emotion ◽  
Emotion Detection ◽  
Face Location ◽  
Human Faces ◽  
Taking Care

Automatic Face Detection innovations have made numerous upgrades in evolving world. Brilliant ATTENDANCE SYSTEM utilizing ongoing face acknowledgment is a genuine world arrangement which accompanies everyday exercises of taking care of understudies participation. The administration of participation framework can be an extraordinary weight on educators in case it is finished by hands.To determine this issue we utilize auto and brilliant participation framework which is by and large executed with the assistance of biometric called Face Detection. The primary execution steps utilized in this kind of framework are face location and perceiving the identified countenances. Face Detection is an interaction where the framework will actually want to recognize the human faces which will be caught by the camera. Here , we execute a computerized participation the board framework for understudies of the class by utilizing face acknowledgment method..

scholarly journals A Simple Yet Reliable Facial Emotion Detection for Campus Environment

2019 ◽  
Vol 8 (3) ◽  
pp. 2477-2481
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Confidence Score ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Face Alignment ◽  
Emotion Detection ◽  
Campus Environment ◽  
The Face ◽  
Haar Cascade

Nowadays, crime incidents like stealing, fighting and harassment often occur in campus leading to serious consequences. Students do not feel secure to study in campus anymore. Thus, a simple facial emotion detection system using a Raspberry Pi is introduced to help mitigating the issue before getting worse in campus. Two algorithms are used for this project including Haar Cascade and Local Binary Pattern (LBP) algorithms. OpenCV is a library that can be used for image processing. LBP algorithm is used for face detection in OpenCV. When a person enters the specified area, the camera will capture the image and detect the image of the person. Then, a rectangular box appears on the face image of the person. The image is automatically sent to the email. The face detection is enhanced by adding a face alignment. The face alignment is used to detect the location of many points on the face. It recognizes the emotions for each face and gives the confidence score. The value 0 of confidence score is the perfect face recognition. Although the system is simple, it is still reliable to be used in a campus environment.

The Adaboost Algorithm Applied in ATM Automatic Identification System

2013 ◽  
Vol 753-755 ◽  
pp. 2941-2944
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Computing Time ◽  
Automatic Identification ◽  
Identification System ◽  
Human Face ◽  
Detection Experiment ◽  
Adaboost Algorithm ◽  
Face Features ◽  
Human Faces

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

Facial Emotion Detection to Assess Learner's State of Mind in an Online Learning System

2020 ◽  
Author(s):  
Moutan Mukhopadhyay ◽  
Saurabh Pal ◽  
Anand Nayyar ◽  
Pijush Kanti Dutta Pramanik ◽  
Niloy Dasgupta ◽  
...  
Keyword(s):  
Online Learning ◽  
Learning System ◽  
Facial Emotion ◽  
Emotion Detection ◽  
State Of Mind

scholarly journals Emotion Detection of Human Face

2019 ◽  
Vol 9 (1) ◽  
pp. 5521-5524 ◽  
Keyword(s):  
Neural Network ◽  
Facial Expressions ◽  
Language Translation ◽  
Online Marketing ◽  
Facial Emotion ◽  
Good Resolution ◽  
Human Beings ◽  
Emotion Detection ◽  
Unsupervised Neural Network ◽  
User Device

Facial emotion analysis is the basic idea to train the system to understand the different facial expressions of human beings. The Facial expressions are recorded by the use of camera which is attached to user device. Additionally this project will be helpful for the online marketing of the products as it can detect the facial expressions and sentiment of the person. It is the study of people sentiment, opinions and emotions. Sentiment analysis is the method by which information is taken from the facial expressions of people in regard to different situations. The main aim is to read the facial expressions of the human beings using a good resolution camera so that the machine can identify the human sentiments. Convolutional neural network is used as an existing system which is unsupervised neural network to replace that with a supervised mechanism which is called supervised neural network. It can be used in gaming sector, unlock smart phones, automated facial language translation etc.

scholarly journals An Innovative Face Detection Based on YCgCr Color Space

2018 ◽  
Author(s):  
Solly Aryza
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Image ◽  
Color Space ◽  
Gaussian Model ◽  
Detection Rates ◽  
Detection Algorithms ◽  
Lighting Conditions ◽  
Wide Range ◽  
Human Faces

It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. The research on detecting human faces in color image and in video sequence has been attracted with more and more people. In this paper, we propose a novel face detection method that achieves better detection rates. The new face detection algorithms based on skin color model in YCgCr chrominance space. Firstly, we build a skin Gaussian model in Cg-Cr color space. Secondly, a calculation of correlation coefficient is performed between the given template and the candidates. Experimental results demonstrate that our system has achieved high detection rates and low false positives over a wide range of facial variations in color, position and varying lighting conditions.

Research and Design of Face Detection Based on OpenCV in CodeBlocks

2012 ◽  
Vol 532-533 ◽  
pp. 974-978
Author(s):  
Xue Cong Lv ◽  
Zheng Bing Zhang
Keyword(s):  
Face Detection ◽  
Full Range ◽  
Detection Algorithm ◽  
Imaging Processing ◽  
Side Face ◽  
Processing Software ◽  
Human Faces ◽  
Research And Design ◽  
And Training ◽  
Face Detector

To implement the problem that the side face detector is slow and its detection rate is low, in this paper, we choose the Adaboost face detection algorithm based on statistics. Then the characteristics of imaging processing software OpenCV and the principle and training flow of Adaboost face detector are introduced. Further, combination with the supplement Haar-like features improved, the full range of face detection based on OpenCV in CodeBlocks is achievement, thereby decreasing the loss of the human faces.

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