scholarly journals A simple multi-feature classification to recognize human emotions in Images

2020 ◽  
Author(s):  
Navin Ipe
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Facial Expressions ◽  
General Pattern ◽  
Facial Feature ◽  
Body Language ◽  
Facial Features ◽  
Verbal Cues ◽  
The Face ◽  
Tone Of Voice

Emotion recognition by the human brain, normally incorporates context, body language, facial expressions, verbal cues, non-verbal cues, gestures and tone of voice. When considering only the face, piecing together various aspects of each facial feature is critical in identifying the emotion. Since viewing a single facial feature in isolation may result in inaccuracies, this paper attempts training neural networks to first identify specific<br>facial features in isolation, and then use the general pattern of expressions on the face to identify the overall emotion. The reason for classification inaccuracies are also examined.<br>

scholarly journals A simple multi-feature classification to recognize human emotions in Images

2020 ◽  
Author(s):  
Navin Ipe
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Facial Expressions ◽  
General Pattern ◽  
Facial Feature ◽  
Body Language ◽  
Facial Features ◽  
Verbal Cues ◽  
The Face ◽  
Tone Of Voice

Emotion recognition by the human brain, normally incorporates context, body language, facial expressions, verbal cues, non-verbal cues, gestures and tone of voice. When considering only the face, piecing together various aspects of each facial feature is critical in identifying the emotion. Since viewing a single facial feature in isolation may result in inaccuracies, this paper attempts training neural networks to first identify specific<br>facial features in isolation, and then use the general pattern of expressions on the face to identify the overall emotion. The reason for classification inaccuracies are also examined.<br>

scholarly journals A simple multi-feature classification to recognize human emotions in Images

2020 ◽  
Author(s):  
Navin Ipe
Keyword(s):  
Neural Networks ◽  
Facial Expressions ◽  
General Pattern ◽  
Facial Feature ◽  
Facial Features ◽  
Feature Classification ◽  
Complex Process ◽  
The Face ◽  
Recognition Of Emotions ◽  
Human Emotions

The recognition of emotions via facial expressions is a complex process of piecing together various aspects of each facial feature. Since viewing a single facial feature in isolation may result in an inaccurate recognition of emotion, this paper attempts training neural networks to first identify specific facial features in isolation, and then use the general pattern of expressions on the face to identify the overall emotion. The technique presented is very basic, and can definitely be improved with more advanced techniques that incorporate time<br>and context.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

scholarly journals Facial Feature Model for a Portrait Video Stylization

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 442 ◽  
Author(s):  
Dongxue Liang ◽  
Kyoungju Park ◽  
Przemyslaw Krompiec
Keyword(s):  
Facial Feature ◽  
Feature Model ◽  
Facial Features ◽  
Outer Face ◽  
Flow Estimation ◽  
Photorealistic Rendering ◽  
Stroke Models ◽  
The Face ◽  
Face Contour ◽  
Human Faces

With the advent of the deep learning method, portrait video stylization has become more popular. In this paper, we present a robust method for automatically stylizing portrait videos that contain small human faces. By extending the Mask Regions with Convolutional Neural Network features (R-CNN) with a CNN branch which detects the contour landmarks of the face, we divided the input frame into three regions: the region of facial features, the region of the inner face surrounded by 36 face contour landmarks, and the region of the outer face. Besides keeping the facial features region as it is, we used two different stroke models to render the other two regions. During the non-photorealistic rendering (NPR) of the animation video, we combined the deformable strokes and optical flow estimation between adjacent frames to follow the underlying motion coherently. The experimental results demonstrated that our method could not only effectively reserve the small and distinct facial features, but also follow the underlying motion coherently.

scholarly journals Web Application for Emotion-based Music Player

2021 ◽  
Vol 9 (12) ◽  
pp. 11-14
Author(s):  
Kamal Naina Soni
Keyword(s):  
Computer Vision ◽  
Human Brain ◽  
Facial Expressions ◽  
Web Application ◽  
Emotional State ◽  
Current State ◽  
The Face ◽  
Current Mood ◽  
The Individual ◽  
Music Player

Abstract: Human expressions play an important role in the extraction of an individual's emotional state. It helps in determining the current state and mood of an individual, extracting and understanding the emotion that an individual has based on various features of the face such as eyes, cheeks, forehead, or even through the curve of the smile. A survey confirmed that people use Music as a form of expression. They often relate to a particular piece of music according to their emotions. Considering these aspects of how music impacts a part of the human brain and body, our project will deal with extracting the user’s facial expressions and features to determine the current mood of the user. Once the emotion is detected, a playlist of songs suitable to the mood of the user will be presented to the user. This can be a big help to alleviate the mood or simply calm the individual and can also get quicker song according to the mood, saving time from looking up different songs and parallel developing a software that can be used anywhere with the help of providing the functionality of playing music according to the emotion detected. Keywords: Music, Emotion recognition, Categorization, Recommendations, Computer vision, Camera

scholarly journals Ability of Children with Learning Disabilities and Children with Autism Spectrum Disorder to Recognize Feelings from Facial Expressions and Body Language

2018 ◽  
Vol 8 (2) ◽  
pp. 10 ◽  
Author(s):  
Alev Girli ◽  
Sıla Doğmaz
Keyword(s):  
Autism Spectrum Disorder ◽  
Learning Disabilities ◽  
Facial Expressions ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Body Language ◽  
The Body ◽  
Spectrum Disorder ◽  
Black And White ◽  
The Face

In this study, children with learning disability (LD) were compared with children with autism spectrum disorder(ASD) in terms of identifying emotions from photographs with certain face and body expressions. The sampleconsisted of a total of 82 children aged 7-19 years living in Izmir in Turkey. A total of 6 separate sets of slides,consisting of black and white photographs, were used to assess participants’ ability to identify feelings – 3 sets forfacial expressions, and 3 sets for body language. There were 20 photographs on the face slides and 38 photographson the body language slides. The results of the nonparametric Mann Whitney-U test showed no significant differencebetween the total scores that children received from each of the face and body language slide sets. It was observedthat the children with LD usually looked at the whole photo, while the children with ASD focused especially aroundthe mouth to describe feelings. The results that were obtained were discussed in the context of the literature, andsuggestions were presented.

scholarly journals Web-Based Sensitivity Training for Interacting with Facial Paralysis

2019 ◽  
Author(s):  
John Michael ◽  
Nicole Zhang ◽  
Kathleen Bogart ◽  
Luke McEllin
Keyword(s):  
Facial Paralysis ◽  
Social Perception ◽  
General Information ◽  
Body Language ◽  
Emotional States ◽  
Web Based ◽  
Facial Cues ◽  
Brief Training ◽  
The Face ◽  
Tone Of Voice

Previous research has shown that observers tend to form inaccurate, negatively biased first impressions of people with facial paralysis (FP). This is likely to be due in part to limits which facial paralysis imposes upon the expression of information about emotional states. It has been hypothesised that this problem may be ameliorated by a training program designed to encourage people who will encounter individuals with FP to focus on other channels of expression rather than the face, e.g. hand gestures, body language, tone of voice and speech content. We tested this hypothesis in two web-based studies. In Study 1, participants in the Trained Condition received tips for understanding/interacting with individuals with FP, and practice in identifying emotions expressed through body language. Participants in the Untrained Condition received only general information about FP, and practice in identifying emotions expressed through facial expression. In the test phase, we compared the two groups’ perception of emotions expressed in videos of individuals with FP, as well as their recall of the content of those videos. The results show that attending to bodily cues and to speech rather than facial cues can improve social perception and reduce bias. Study 2 tested participants in the Trained group two months later. The results show that the effects of the training did not persist. Taken together, our findings support the hypothesis that even brief training in attending to non-facial cues when interacting with individuals with FP can improve social perception and reduce bias, but that these effects do not persist over longer time periods in the absence of further training.

scholarly journals DEVELOPMENT A SYSTEM FOR CLASSIFYING AND RECOGNIZING PERSON’S FACE

2020 ◽  
Vol 4 (4(73)) ◽  
pp. 15-24
Author(s):  
S.N. Boranbayev ◽  
M.S. Amirtayev
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Machine Learning Algorithms ◽  
Facial Features ◽  
Histogram Of Oriented Gradients ◽  
Image Encoding ◽  
Linear Classifier ◽  
The Face ◽  
Special Points

The purpose of this article is to summarize the knowledge gained in the development and implementation of a neural network for facial recognition. Neural networks are used to solve complex tasks that require analytical calculations similar to what the human brain does. Machine learning algorithms are the foundation of a neural network. As input, the algorithm receives an image with people's faces, then searches for faces in this image using HOG (Histogram of oriented gradients). The result is images with explicit face structures. To determine unique facial features, the Face landmark algorithm is used, which finds 68 special points on the face. These points can be used to centerthe eyes and mouth for more accurate encoding. To get an accurate “face map” consisting of 128 dimensions, you need to use image encoding. Using the obtained data, the convolutional neural network can determine people's faces using the SVM linear classifier algorithm.

scholarly journals New Method for Locker Door with Face Security System Using Backpropagation Algorithm

2018 ◽  
Vol 7 (2.13) ◽  
pp. 402
Author(s):  
Y Yusmartato ◽  
Zulkarnain Lubis ◽  
Solly Arza ◽  
Zulfadli Pelawi ◽  
A Armansah ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Back Propagation ◽  
The Face ◽  
Artificial Neural ◽  
Hidden Layer

Lockers are one of the facilities that people use to store stuff. Artificial neural networks are computational systems where architecture and operations are inspired by the knowledge of biological neurons in the brain, which is one of the artificial representations of the human brain that always tries to stimulate the learning process of the human brain. One of the utilization of artificial neural network is for pattern recognition. The face of a person must be different but sometimes has a shape similar to the face of others, because the facial pattern is a good pattern to try to be recognized by using artificial neural networks. Pattern recognition on artificial neural network can be done by back propagation method. Back propagation method consists of input layer, hidden layer and output layer.  

Emotions Are Contagious

2020 ◽  
Author(s):  
Sigal Barsade ◽  
Keyword(s):  
Facial Expressions ◽  
Positive Emotions ◽  
Body Language ◽  
Negative Effects ◽  
Family Dinner ◽  
Phone Calls ◽  
Dinner Table ◽  
The Family ◽  
Improved Performance ◽  
Tone Of Voice

You're at the family dinner table. Your spouse worries that a friend's business is struggling. Then your son complains about his math homework and your inability to help, and your daughter asks when she will see her friends again. As the meal progresses, you can feel everyone becoming more and more anxious. Emotions are contagious. We automatically mimic each other's facial expressions, body language, and tone of voice. Next, we actually feel the emotions we mimicked and begin to act on them. Without our realizing what's happening, feelings can escalate, as we “catch” them from other people, who catch them back from us, in an increasing spiral. While emotions spread more easily in person, they also get transmitted through social media, phone calls, emails, and video chats. In fact, negative emotions related to isolation may make us even more susceptible. Luckily, knowledge is a form of inoculation. Just being aware of emotional contagion can reduce its negative effects. And positive emotions transfer just as easily as negative ones. The spread of positive emotions leads to greater cooperation, less conflict, and improved performance.

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