Facial Emotion Recognition: A Deep Learning approachAshwinRachha
Neural Networks lie at the apogee of Machine Learning algorithms. With a large set of data and automatic feature selection and extraction process, Convolutional Neural Networks are second to none. Neural Networks can be very effective in classification problems.
Facial Emotion Recognition is a technology that helps companies and individuals evaluate customers and optimize their products and services by most relevant and pertinent feedback.
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
Facial Emotion Recognition using Convolution Neural NetworkYogeshIJTSRD
Facial expression plays a major role in every aspect of human life for communication. It has been a boon for the research in facial emotion with the systems that give rise to the terminology of human computer interaction in real life. Humans socially interact with each other via emotions. In this research paper, we have proposed an approach of building a system that recognizes facial emotion using a Convolutional Neural Network CNN which is one of the most popular Neural Network available. It is said to be a pattern recognition Neural Network. Convolutional Neural Network reduces the dimension for large resolution images and not losing the quality and giving a prediction output whats expected and capturing of the facial expressions even in odd angles makes it stand different from other models also i.e. it works well for non frontal images. But unfortunately, CNN based detector is computationally heavy and is a challenge for using CNN for a video as an input. We will implement a facial emotion recognition system using a Convolutional Neural Network using a dataset. Our system will predict the output based on the input given to it. This system can be useful for sentimental analysis, can be used for clinical practices, can be useful for getting a persons review on a certain product, and many more. Raheena Bagwan | Sakshi Chintawar | Komal Dhapudkar | Alisha Balamwar | Prof. Sandeep Gore "Facial Emotion Recognition using Convolution Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39972.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/39972/facial-emotion-recognition-using-convolution-neural-network/raheena-bagwan
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
Emotion Detection using Artificial Intelligence presentation by Aryan Trisal.
In this ppt you will learn about emotion detection using AI and how will it change the world.
IF U WANT A PPT MADE AT VERY LOW PRICES CONTACT ME ON LINKEDIN -www.linkedin.com/in/aryan-trisal-420253190
Face detection basedon image processing by using the segmentation methods for detection of the various types of the faces to helpfull for the many different careers and it will easy to do.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
Emotion recognition using image processing in deep learningvishnuv43
User’s emotion using its facial expressions will be detected. These expressions can be derived from the live feed via system's camera or any pre-existing image available in the memory. Emotions possessed by humans can be recognized and has a vast scope of study in the computer vision industry upon which several researches have already been done.
We propose a compact CNN model for facial expression recognition.
The work has been implemented using Python Open Source Computer Vision Library (OpenCV) and NumPy,pandas,keras packages. The scanned image (testing dataset) is being compared to training dataset and thus emotion is predicted.
Facial emoji recognition is a human computer interaction system. In recent times, automatic face recognition or facial expression recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and similar fields. Facial emoji recognizer is an end user application which detects the expression of the person in the video being captured by the camera. The smiley relevant to the expression of the person in the video is shown on the screen which changes with the change in the expressions. Facial expressions are important in human communication and interactions. Also, they are used as an important tool in studies about behavior and in medical fields. Facial emoji recognizer provides a fast and practical approach for non meddlesome emotion detection. The purpose was to develop an intelligent system for facial based expression classification using CNN algorithm. Haar classifier is used for face detection and CNN algorithm is utilized for the expression detection and giving the emoticon relevant to the expression as the output. N. Swapna Goud | K. Revanth Reddy | G. Alekhya | G. S. Sucheta ""Facial Emoji Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23166.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23166/facial-emoji-recognition/n-swapna-goud
Facial Emotion Recognition: A Deep Learning approachAshwinRachha
Neural Networks lie at the apogee of Machine Learning algorithms. With a large set of data and automatic feature selection and extraction process, Convolutional Neural Networks are second to none. Neural Networks can be very effective in classification problems.
Facial Emotion Recognition is a technology that helps companies and individuals evaluate customers and optimize their products and services by most relevant and pertinent feedback.
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
Facial Emotion Recognition using Convolution Neural NetworkYogeshIJTSRD
Facial expression plays a major role in every aspect of human life for communication. It has been a boon for the research in facial emotion with the systems that give rise to the terminology of human computer interaction in real life. Humans socially interact with each other via emotions. In this research paper, we have proposed an approach of building a system that recognizes facial emotion using a Convolutional Neural Network CNN which is one of the most popular Neural Network available. It is said to be a pattern recognition Neural Network. Convolutional Neural Network reduces the dimension for large resolution images and not losing the quality and giving a prediction output whats expected and capturing of the facial expressions even in odd angles makes it stand different from other models also i.e. it works well for non frontal images. But unfortunately, CNN based detector is computationally heavy and is a challenge for using CNN for a video as an input. We will implement a facial emotion recognition system using a Convolutional Neural Network using a dataset. Our system will predict the output based on the input given to it. This system can be useful for sentimental analysis, can be used for clinical practices, can be useful for getting a persons review on a certain product, and many more. Raheena Bagwan | Sakshi Chintawar | Komal Dhapudkar | Alisha Balamwar | Prof. Sandeep Gore "Facial Emotion Recognition using Convolution Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39972.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/39972/facial-emotion-recognition-using-convolution-neural-network/raheena-bagwan
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
Emotion Detection using Artificial Intelligence presentation by Aryan Trisal.
In this ppt you will learn about emotion detection using AI and how will it change the world.
IF U WANT A PPT MADE AT VERY LOW PRICES CONTACT ME ON LINKEDIN -www.linkedin.com/in/aryan-trisal-420253190
Face detection basedon image processing by using the segmentation methods for detection of the various types of the faces to helpfull for the many different careers and it will easy to do.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
Emotion recognition using image processing in deep learningvishnuv43
User’s emotion using its facial expressions will be detected. These expressions can be derived from the live feed via system's camera or any pre-existing image available in the memory. Emotions possessed by humans can be recognized and has a vast scope of study in the computer vision industry upon which several researches have already been done.
We propose a compact CNN model for facial expression recognition.
The work has been implemented using Python Open Source Computer Vision Library (OpenCV) and NumPy,pandas,keras packages. The scanned image (testing dataset) is being compared to training dataset and thus emotion is predicted.
Facial emoji recognition is a human computer interaction system. In recent times, automatic face recognition or facial expression recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and similar fields. Facial emoji recognizer is an end user application which detects the expression of the person in the video being captured by the camera. The smiley relevant to the expression of the person in the video is shown on the screen which changes with the change in the expressions. Facial expressions are important in human communication and interactions. Also, they are used as an important tool in studies about behavior and in medical fields. Facial emoji recognizer provides a fast and practical approach for non meddlesome emotion detection. The purpose was to develop an intelligent system for facial based expression classification using CNN algorithm. Haar classifier is used for face detection and CNN algorithm is utilized for the expression detection and giving the emoticon relevant to the expression as the output. N. Swapna Goud | K. Revanth Reddy | G. Alekhya | G. S. Sucheta ""Facial Emoji Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23166.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23166/facial-emoji-recognition/n-swapna-goud
There are several ways to detect emotion. We can briefly list them here:
EEG + BCI
ECG + Cardiovascular signals
Electrodermal activity
Speech + Voice intonation
Facial expressions
Body language
Now we can take a look at their applications!
Review of facial expression recognition system and used datasetseSAT Journals
Abstract The human face is main part to recognize the individuals as well as provides the important information, current state of user behavior through their different expressions. Therefore, in biometric area of the research, automatically face & face expression recognition attracts researcher’s interest. The other areas which use such technique are computer science medicine, psychology etc. Usually face recognition system is consisting of many internal tasks. Face detection is thefirst task of such systems. Due to different variations across the human faces, the process of detecting face becomes complex. But with help of different modeling methods, it becomes possible to recognize the face and hence different face expressions. This paperpresents a literature review over the techniques and methods used for facial expression recognition. Also, different facial expression datasets available for the research or testing of existing methods of facial expression recognition are discussed. Keywords: Facial Expression, Face Detection, Features Extraction, Recognition, datasets.
Facial expression identification by using features of salient facial landmarkseSAT Journals
Abstract
Facial expression recognition/identification (FER) systems plays vital role in the field of biometrics. Localizing the facial components accurately is a challenging task in image analysis and computer vision. Accurate detection of face and facial components gives effective performance with classification of expressions. This paper proposes feature based facial recognition system using JAFFE and CK databases. 18 facial landmarks were located using Haar cascade classifier. The distances between 12 points were extracted as features. These features were classified using SVM and K-NN classifier and comparison based on accuracy and execution time is done. The proposed algorithm gives better performance.
This paper proposed a facial expression recognition approach based on Gabor wavelet transform. Gabor wavelet filter is first used as pre-processing stage for extraction of the feature vector representation. Dimensionality of the feature vector is reduced using Principal Component Analysis and Local binary pattern (LBP) Algorithms. Experiments were carried out of The Japanese female facial expression (JAFFE) database. In all experiments conducted on JAFFE database, results obtained reveal that GW+LBP has outperformed other approaches in this paper with Average recognition rate of 90% under the same experimental setting.
Paper helps to learner to know about various bio metrics and comparison between them. this paper provide researchers a brief information so that they can choose their research area in biometric based on the comparison
PROPOSAL DEFENSE 2-24-16-- Created using PowToon -- Free sign up at http://www.powtoon.com/ -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Learning to express emotion, in this case the emotion of sadness.
Based on Charles Darwin and Paul Ekman work of facial expression of emotion, this presentation leads into the embodied expression of sadness
Comparative Study of Lip Extraction Feature with Eye Feature Extraction Algor...Editor IJCATR
In recent time, along with the advances and new inventions in science and technology, fraud people and identity thieves are
also becoming smarter by finding new ways to fool the authorization and authentication process. So, there is a strong need of efficient
face recognition process or computer systems capable of recognizing faces of authenticated persons. One way to make face recognition
efficient is by extracting features of faces. This paper is to compare the relative efficiency of Lip Extraction and Eye extraction feature
for face recognition in biometric devices. Importance of this paper is to bring to the light which Feature Extraction method provides
better results under various conditions. For recognition experiments, I used face images of persons from different sets of YALE
database. In my dataset, there are total 132 images consisting of 11 persons & 12 face images of each person.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...AM Publications
Facial expression analysis is a remarkable and demanding problem, and impacts significant applications in various fields like human-computer interaction and data-driven animation. Developing an efficient facial representation from the original face images is a crucial step for achieving facial expression recognition. Facial representation based on statistical local features, Local Binary Patterns (LBP) is practically assessed. Several machine learning techniques were thoroughly observed on various databases. LBP features- which are effectual and competent for facial expression recognition are generally used by researchers Cohn Kanade is the database for present work and the programming language used is MATLAB. Firstly, face area is divided in small regions, by which histograms, Local Binary Patterns (LBP) are extracted and then concatenated into single feature vector. This feature vector outlines a well-organized representation of face and is helpful in determining the resemblance among images.
Facial Expression Recognition Using Local Binary Pattern and Support Vector M...AM Publications
Facial expression analysis is a remarkable and demanding problem, and impacts significant applications in various fields like human-computer interaction and data-driven animation. Developing an efficient facial representation from the original face images is a crucial step for achieving facial expression recognition. Facial representation based on statistical local features, Local Binary Patterns (LBP) is practically assessed. Several machine learning techniques were thoroughly observed on various databases. LBP features- which are effectual and competent for facial expression recognition are generally used by researchers Cohn Kanade is the database for present work and the programming language used is MATLAB. Firstly, face area is divided in small regions, by which histograms, Local Binary Patterns (LBP) are extracted and then concatenated into single feature vector. This feature vector outlines a well-organized representation of face and is helpful in determining the resemblance among images.
A study of techniques for facial detection and expression classificationIJCSES Journal
Automatic recognition of facial expressions is an important component for human-machine interfaces. It
has lot of attraction in research area since 1990's.Although humans recognize face without effort or
delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
methods of facial detection,facial feature extraction and classification.
This paper describes for a robust face recognition system using skin segmentation technique. This paper addresses the problem of detecting faces in color images in the presence of various lighting conditions. In this paper the face is preprocessed using histogram equalization to avoid illumination problems and then is detected using skin segmentation method. The principal component analysis using neural network is used to recognize the extracted facial features.
A Review on Feature Extraction Techniques and General Approach for Face Recog...Editor IJCATR
In recent time, alongwith the advances and new inventions in science and technology, fraud people and identity thieves are
also becoming smarter by finding new ways to fool the authorization and authentication process. So, there is a strong need of efficient
face recognition process or computer systems capable of recognizing faces of authenticated persons. One way to make face recognition
efficient is by extracting features of faces. Several feature extraction techniques are available such as template based, appearancebased,
geometry based, color segmentation based, etc. This paper presents an overview of various feature extraction techniques
followed in different reasearches for face recognition in the field of digital image processing and gives an approach for using these
feature extraction techniques for efficient face recognition
REVIEW OF FACE DETECTION SYSTEMS BASED ARTIFICIAL NEURAL NETWORKS ALGORITHMSijma
Face detection is one of the most relevant applications of image processing and biometric systems.
Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition.
There is lack of literature surveys which give overview about the studies and researches related to the using
of ANN in face detection. Therefore, this research includes a general review of face detection studies and
systems which based on different ANN approaches and algorithms. The strengths and limitations of these
literature studies and systems were included also.
Review of face detection systems based artificial neural networks algorithmsijma
Face detection is one of the most relevant applications of image processing and biometric systems.
Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition.
There is lack of literature surveys which give overview about the studies and researches related to the using
of ANN in face detection. Therefore, this research includes a general review of face detection studies and
systems which based on different ANN approaches and algorithms. The strengths and limitations of these
literature studies and systems were included also.
PRE-PROCESSING TECHNIQUES FOR FACIAL EMOTION RECOGNITION SYSTEMIAEME Publication
Humans share a universal and fundamental set of emotions which are exhibited through consistent facial expressions. Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature extraction and classification technique for emotion recognition is still an open problem. Image pre-processing and normalization is significant part of face recognition systems. Changes in lighting conditions produces dramatically decrease of recognition performance. In this paper, the image pre-processing techniques like K-Nearest Neighbor, Cultural Algorithm and Genetic Algorithm are used to remove the noise in the facial image for enhancing the emotion recognition. The performance of the preprocessing techniques are evaluated with various performance metrics.
This thesis proposed the face recognition method using common and most sophisticated face recognition algorithm to maintain and manage image or photograph database. Here Photograph Image means Image with human faces. In this thesis, image’s faces are extracted and classified and indexed. Indexed values are later used for creating logical database of images. This indexed values will be used to search and display related images. This is also features about the usage of the indexed values to identify or search one face with combinations of other faces and it will help police to investigate in more details. In this thesis a feature is extracted using principal component analysis and then classification by creation neural network. Recently, the PCA (Principal Component Analysis) has been extensively employed for face recognition algorithms. It is one of the most popular representation methods for a face image. It not only reduces the dimensionality of the image, but also retains some of the variations in the image data. We run our algorithm for face recognition application using principal component analysis. The system produces promising results for face verification and face recognition. Demonstrate the recognition accuracy for given number of input pattern. Our approach treats face recognition problem as intrinsically(inherent) two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional(3-D) geometry, taking advantage of fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as “eigenfaces”, because they are the eigenvectors (principal components) of the set of faces. Computer model of face recognition in particular, are interesting because they can contribute not only to theoretical insights but also to the practical applications. Computer that recognizes faces could be applied to a wide variety of problems like criminal identification, security system, image and film processing and human-computer interaction.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
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Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
Eureka, I found it! - Special Libraries Association 2021 Presentation
Facial expression recognition based on image feature
1. FACIAL EXPRESSION RECOGNITION
BASED ON
IMAGE FEATURE
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
ANWESHA PAUL
ID: 110206
TASNIM TARANNUM
ID: 110216
PRESENTED BY:
2. LOGO2
Overview
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Facial Expression &
Facial Expression Recognition
Related Works
Problems Of Existing System
Motivation
Proposed Method
Conclusion
Reference
3. LOGO3
Facial Expression
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Powerful, natural and immediate means for human to
communicate their emotions.
Vital part of communication.
Widely recognized in social interaction.
6. LOGO6
Expression Recognition
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Face
Acquisition
Facial Data
Extraction &
Representation
Facial
Expression
Recognition
Face
Detection
Head
Pose
Estimation
Feature-
based
Appearance
-based
Frame-
based
Sequence
-based
Fig 2: Basic Structure of Facial Expression Recognition.
7. LOGO7
Related Work
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Reference
Image
Acquisition
Feature
Extraction
Classification
Recognition
Performance
Neeta Sarode
et al.
[1]
Gray scale
image to
recognize four
expressions
2D appearance-
based local
approach
Euclidean
distance
Accuracy rate
81%
Rupinder
Saini et al.
[2]
PCA, Gabor
wavelet, PCA
with SVD
Euclidean
distance, PCA
[1] Neeta Sarode, Prof. Shalini Bhatia, “Facial Expression Recognition”, (IJCSE) International Journal on Computer Science and
Engineering, Vol. 02, No. 05, 2010.
[2] Rupinder Saini, Narinder Rana, “Facial Expression Recognition Techniques, Database & Classifiers”, International Journal of
Advances in Computer Science and Communication Engineering (IJACSCE), Vol. 2, Issue 2, June 2014.
8. LOGO8
Related Work contd..
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Reference
Image
Acquisition
Feature
Extraction
Classification
Recognition
Performance
Jeemoni
Kalita et al.
[3]
60 samples with
various
expression of
RGB color
image
Manually
extracted and
Eigenvector
based distributed
feature
Euclidean
distance
Recognition
rate 95% &
process time
0.0295 sec
Ajit P.Gosavi
et al.
[4]
Real database
image to
recognize five
basic emotions
PCA (Principal
Component
analysis) with
SVD (Singular
Value
Decomposition)
Euclidean
distance
Avg. accuracy
89.70% & avg.
recognition rate
65.42%
[3] Jeemoni Kalita, Karen Das, “Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean
Distance Based Decision Making Technique”, (IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 4, No. 2, 2013.
[4] Ajit P.Gosavi and S.R. Khot, “Facial Expression Recognition uses Principal Component Analysis with Singular Value
Decomposition”, International Journal of Advance Research in Computer Science and Management Studies Vol. 1, Issue 6,
November 2013.
9. LOGO9
Related Work contd..
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Reference
Image
Acquisition
Feature
Extraction
Classification
Recognition
Performance
Akshat
Garget et al.
[7]
Gray scale
image
PCA (Principal
Component
analysis)
Euclidean
distance & PCA
Accuracy rate
89.0%
Mahesh
Kumbhar et
al.
[8]
JAFFE [6]
database image
PCA(Principal
Component
analysis), Gabor
wavelet
Euclidean
distance
Recognition
rate 60% to
70%
[
7] Akshat Garg, Vishakha Choudhary, “Facial Expression Recognition Using Principal Component Analysis”, International Journal
of Scientific Research Engineering &Technology (IJSRET), Vol. 1 Issue4, July 2012.
[8] Mahesh Kumbhar, Ashish Jadhav, Manasi Patil, “Facial Expression Recognition Based on Image Feature”, International
Journal of Computer and Communication Engineering, Vol. 1, No. 2, July 2012.
[6] JAFFE (Japanese Female Facial Expression) Face Database. Available: [Online] http:// www.kasrl.org/jaffe.html
10. LOGO10
Problems Of Existing System
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Don’t contain enough feature points.[1]
PCA-based face recognition systems are hard to scale
up.[4]
Color image burdensome.[4]
[1] Neeta Sarode, Prof. Shalini Bhatia, “Facial Expression Recognition”, (IJCSE) International Journal on Computer
Science and Engineering, Vol. 02, No. 05, 2010.
[4] Ajit P.Gosavi and S.R. Khot, “Facial Expression Recognition uses Principal Component Analysis with Singular Value
Decomposition”, International Journal of Advance Research in Computer Science and Management Studies Vol. 1, Issue
6, November 2013.
11. LOGO11
Problems Of Existing System
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Cropping manually is time killing.[3]
Inabilities (different angles and different reasons).
[3] Jeemoni Kalita, Karen Das, “Recognition of Facial Expression Using Eigenvector Based Distributed Features
and Euclidean Distance Based Decision Making Technique”, (IJACSA) International Journal of Advanced
Computer Science and Applications, Vol. 4, No. 2, 2013.
12. LOGO12
Motivation
COMPUTER SCIENCE & ENGINEERING
Recognize facial expression as like a human.
Recognize six basic expressions.
Increase the accuracy rate.
13. LOGO13
Proposed Method
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Block diagram of proposed system:
Image
Acquisition
Feature
Extraction
Classifier
Happy
Sad
Surprise
Angry
Fear
Disgust
Fig 3: Block diagram.
14. LOGO14
Proposed Method
contd….
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Image Acquisition:
Convert the color image into gray scale image.
Fig 4: RGB- color image converted into gray scale image.
16. LOGO16
Proposed Method
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Feature Extraction:
• Edge projection ∗.
• Segmentation using Laplacian of Gaussian
operator at zero threshold.
.
17. LOGO17
Proposed Method
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Classifier:
• Euclidean distance based on geometrical
relationship.
• The feature vector V.
𝑉 = 𝑉𝑑0 𝑉𝑑1 𝑉𝑑2 𝑉𝑤 𝑉ℎ 𝑉𝑢𝑙 𝑉𝑙𝑙
18. LOGO18
Proposed Method
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Classifier:
Here,
Vd0 = distance of eyebrow,
Vd1 = distance between right eyebrow and nose tip,
Vd2 = distance between left eyebrow and nose,
Vw = mouth width,
Vh = mouth height,
Vul = upper lip curvature,
Vll = lower lip curvature.
Fig 6: Geometrical parameters of the face, forming the feature vector.
19. LOGO19
Proposed Method
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Decision Making Techniques:
• Feature vector calculation.
• Observe each component of feature vector.
• Comparison between testing image and neutral
image.
20. LOGO20
Proposed Method
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Result Analysis:
Comparison between the testing image with it’s
corresponding images from training database [6].
[6] JAFFE (Japanese Female Facial Expression) Face Database. Available: [Online] http://
www.kasrl.org/jaffe.html
21. LOGO21
Conclusion
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Recognize six basic facial expressions.
Future work: Will develop the same in real time
videos.
22. LOGO22
Reference
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
[1] Neeta Sarode, Prof. Shalini Bhatia, “Facial Expression Recognition”, (IJCSE) International Journal on
Computer Science and Engineering, Vol. 02, No. 05, 2010.
[2] Rupinder Saini, Narinder Rana, “Facial Expression Recognition Techniques, Database & Classifiers”,
International Journal of Advances in Computer Science and Communication Engineering (IJACSCE),
Vol. 2, Issue 2, June 2014.
[3] Jeemoni Kalita, Karen Das, “Recognition of Facial Expression Using Eigenvector Based Distributed
Features and Euclidean Distance Based Decision Making Technique”, (IJACSA) International Journal
of Advanced Computer Science and Applications, Vol. 4, No. 2, 2013.
[4] Ajit P.Gosavi and S.R. Khot, “Facial Expression Recognition uses Principal Component Analysis with
Singular Value Decomposition”, International Journal of Advance Research in Computer Science and
Management Studies Vol. 1, Issue 6, November 2013.
[6] JAFFE (Japanese Female Facial Expression) Face Database. Available: [Online] http://
www.kasrl.org/jaffe.html
[7] Akshat Garg, Vishakha Choudhary, “Facial Expression Recognition Using Principal Component
Analysis”, International Journal of Scientific Research Engineering &Technology (IJSRET), Vol. 1
Issue4, July 2012.
[8] Mahesh Kumbhar, Ashish Jadhav, Manasi Patil, “Facial Expression Recognition Based on Image
Feature”, International Journal of Computer and Communication Engineering, Vol. 1, No. 2, July
2012.