Facial Emotion Detection Using Deep Learning and Haar Cascade Face Identification Algorithm

2021 ◽  
pp. 163-180
Author(s):  
Bhavya Alankar ◽  
Mohammad Sharay Ammar ◽  
Harleen Kaur
Keyword(s):  
Deep Learning ◽  
Facial Emotion ◽  
Face Identification ◽  
Identification Algorithm ◽  
Emotion Detection ◽  
Haar Cascade

scholarly journals Facial Emotion Recognition for Autism Children

2020 ◽  
Vol 9 (7) ◽  
pp. 1274-1278
Keyword(s):  
Facial Expressions ◽  
Autism Spectrum ◽  
Facial Emotion ◽  
Expression Recognition ◽  
Emotion Detection ◽  
Age And Gender ◽  
Social Media Platforms ◽  
Haar Cascade ◽  
And Gender ◽  
Cascade Method

Nowadays Autism children find it difficult to interact socially with people emotions and make themselves isolated. This paper proposes Emotion detection for Autism spectrum disorder children (ASD). It is self-possessed of python libraries Open CV, Haar-cascade method and Age and gender prediction. Conversely, most existing methods rely on the detection of facial expressions of people in social media platforms such as snapchat use facial recognition technology and also detecting facial emotions from their Facial expressions in image. And for a better involvement of the children’s social behaviour, here a face is captured in real time and age, gender and emotions are predicted by Facial expression recognition (FER). This proposed system helps to improve the Autism children behaviour as they often observe the facial expressions of humans and try to imitate their emotions which make a huge difference in their behaviour.

Human Facial Emotion Detection Using Deep Learning

2021 ◽  
pp. 1417-1427
Author(s):  
Dharma Karan Reddy Gaddam ◽  
Mohd Dilshad Ansari ◽  
Sandeep Vuppala ◽  
Vinit Kumar Gunjan ◽  
Madan Mohan Sati
Keyword(s):  
Deep Learning ◽  
Facial Emotion ◽  
Emotion Detection

scholarly journals Emotion Detection on live video using Deep Learning

2020 ◽  
Vol 9 (11) ◽  
pp. 74-77
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Emotion Detection ◽  
Cascade Algorithm ◽  
Live Video ◽  
Haar Cascade

In modern days, feeling exposure is a ground of curiosity and is used in fields such as cross-examining prisoners and teenagers observing human-computer relations. The anticipated work designates the exposure of mortal sentiments from an instantaneous video or stationary video with the help of a convolution neural network (CNN) and haar cascade algorithm. The foremost part of the announcement constitutes field appearance. The suggested work aims to categorize a given video or a live video into one of the emotions (natural, angry, happy, fearful, disgusted, sad, surprise). Our work also distinguishes multiple faces from live video and organize their emotions. Our recommended work also imprisonments the pictures from the video every second, hoard them into a file, and generates a video from those pictures along with their respective.

scholarly journals Deep Learning Model to Analyze Customer’s Satisfaction

2020 ◽  
Vol 9 (4) ◽  
pp. 1709-1714
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Facial Emotion ◽  
Support Vector ◽  
Emotion Detection ◽  
Emotional Faces ◽  
Neutral Face ◽  
Key Points ◽  
Manual Methods ◽  
Deep Learning Model

Nowadays, measuring customer satisfaction is an important strategic tool for companies; many manual methods exist to measure customer’s satisfaction. However, the results have not effective and efficient. In this paper, we propose a new method for facial emotion detection to recognize customer’s satisfaction using a deep learning model. We used a convolutional neural network to detect facial key points. These key points help us to extract geometric features from customer’s emotional faces. Indeed, we computed distances between neutral face and negative or positive feedback. After that, we classified these distances by using Support Vector Machine (SVM), KNN, Random Forest, and Decision Tree. To evaluate the performance of our approach, we tested our algorithm by using FACEDB and JAFFE datasets. We found that SVM is the most performant classifier. We obtained 96% as accuracy by using FACEDB dataset and 95% by using JAFFE dataset.

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.

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