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.
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
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.
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
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.
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 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.
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
The slide was prepared on the purpose of presentation of our project face detection highlighting the basics of theory used and project details like goal, approach. Hope it's helpful.
Presentation on Face detection and recognition - Credits goes to Mr Shriram, "https://www.hackster.io/sriram17ei/facial-recognition-opencv-python-9bc724"
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
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
Driver drowsiness monitoring system using visual behavior and Machine Learning.AasimAhmedKhanJawaad
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
Attendance Management System using Face RecognitionNanditaDutta4
The project ppt presentation is made for the academic session for the completion of the work from Bharati Vidyapeeth Deemed University(IMED) MCA department
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 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.
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
The slide was prepared on the purpose of presentation of our project face detection highlighting the basics of theory used and project details like goal, approach. Hope it's helpful.
Presentation on Face detection and recognition - Credits goes to Mr Shriram, "https://www.hackster.io/sriram17ei/facial-recognition-opencv-python-9bc724"
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
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
Driver drowsiness monitoring system using visual behavior and Machine Learning.AasimAhmedKhanJawaad
Drowsy driving is one of the major causes of road accidents and death. Hence, detection of
driver’s fatigue and its indication is an active research area. Most of the conventional methods are
either vehicle based, or behavioral based or physiological based. Few methods are intrusive and
distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low
cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. In the
developed system, a webcam records the video and driver’s face is detected in each frame employing
image processing techniques. Facial landmarks on the detected face are pointed and subsequently the
eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their
values, drowsiness is detected based on developed adaptive thresholding. Machine learning
algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and
specificity of 100% has been achieved in Support Vector Machine based classification.
Attendance Management System using Face RecognitionNanditaDutta4
The project ppt presentation is made for the academic session for the completion of the work from Bharati Vidyapeeth Deemed University(IMED) MCA department
Methods of Optimization in Machine LearningKnoldus Inc.
In this session we will discuss about various methods to optimise a machine learning model and, how we can adjust the hyper-parameters to minimise the cost function.
A DEEP LEARNING APPROACH FOR SEMANTIC SEGMENTATION IN BRAIN TUMOR IMAGESPNandaSai
Digital image processing is vast fields which can be using various applications. Which include Detection of criminal face, fingerprint authentication system, in medical field, object recognition etc. Brain tumor detection plays an important role in medical field. Brain tumor detection is detection of tumor affected part in the brain along with its shape size and boundary, so it useful in medical field.
Segmentation and the subsequent quantitative assessment of lesions in medical images provide valuable information for the analysis of neuropathologist and are important for planning of treatment strategies, monitoring of disease progression and prediction of patient outcome. For a better understanding of the pathophysiology of diseases, quantitative imaging can reveal clues about the disease characteristics and effects on particular anatomical structures
This is the Bangla Handwritten Digit Recognition Report. you can see this report for your helping hand.
**Bengali is the world's fifth most spoken language, with 265 million native and non-native speakers accounting for 4% of the global population.
**Despite the large number of Bengali speakers, very little research has been conducted on Bangali handwritten digit recognition.
**The application of the BHwDR system is wide from postal code digit recognition to license plate recognition, digit recognition in cheques in the banking system to exam paper registration number recognition.
Fast and robust tracking of multiple faces is receiving increased attention from computer vision researchers as it finds potential applications in many fields like video surveillance and computer mediated video conferencing. Real-time tracking of multiple faces in high resolution videos involve three basic tasks namely initialization, tracking and display. Among these, tracking is quite compute intensive as it involves particle filtering that won’t yield a real time performance if we use a conventional CPU based system alone.
This paper presents a study of the efficiency and performance speedup achieved by applying Graphics Processing Units for Face Recognition Solutions. We explore one of the possibilities of parallelizing and optimizing a well-known Face Recognition algorithm, Principal Component Analysis (PCA) with Eigenfaces. In recent years, the Graphics Processing Units (GPU) has been the subject of extensive research and the computation speed of GPUs has been rapidly increasing.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
2. Why Emotion Detection?
● The motivation behind choosing this topic specifically lies in the huge
investments large corporations do in feedbacks and surveys but fail to get
equitable response on their investments.
● Emotion Detection through facial gestures is a technology that aims to improve
product and services performance by monitoring customer behavior to certain
products or service staff by their evaluation
3. Some Companies that make us of emotion detection...
● While Disney uses emotion-detection tech to find out opinion on a completed
project, other brands have used it to directly inform advertising and digital
marketing.
● Kellogg’s is just one high-profile example, having used Affectiva’s software to test
audience reaction to ads for its cereal.
● Unilever does this, using HireVue’s AI-powered technology to screen prospective
candidates based on factors like body language and mood. In doing so, the
company is able to find the person whose personality and characteristics are best
suited to the job.
4. Emotion Expression Recognition Using SVM
What are Support Vector Machines?
SVMs plot the training vectors in high-dimensional
feature space, and label each vector with its class. A
hyperplane is drawn between the training vectors
that maximizes the distance between the different
classes. The hyperplane is determined through a
kernel function (radial basis, linear, polynomial or
sigmoid), which is given as input to the classification
software. [1999][Joachims, 1998b].
5. Facial Expression Detection using Fuzzy Classifier
● The algorithm is composed of three main
stages: image processing stage and facial
feature extraction stage, and emotion
detection stage.
● In image processing stage, the face region
and facial component is extracted by using
fuzzy color filter, virtual face model, and
histogram analysis method.
● The features for emotion detection are
extracted from facial component in facial
feature extraction stage.
7. Facial Emotion Recognition by CNN
➽ Steps:
1. Data Preprocessing
2. Image Augmentation
3. Feature Extraction
4. Training
5. Validation
8. Dataset Description
The data consists of 48x48 pixel grayscale images of faces. The faces have been
categorized into facial expression in to one of seven categories (0=Angry, 1=Disgust,
2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral).
The training set consists of 28,709 examples. The public test set used for the
leaderboard consists of 3,589 examples. The final test set, which was used to determine
the winner of the competition, consists of another 3,589 examples.This dataset was
prepared by Pierre-Luc Carrier and Aaron Courville, as part of an ongoing research
project.
9. Data Preprocessing
● The fer2013.csv consists of three columns namely emotion, pixels and purpose.
● The column in pixel first of all is stored in a list format.
● Since computational complexity is high for computing pixel values in the range of
(0-255), the data in pixel field is normalized to values between [0-1].
● The face objects stored are reshaped and resized to the mentioned size of 48 X 48.
● The respective emotion labels and their respective pixel values are stored in
objects.
● We use scikit-learn’s train_test_split() function to split the dataset into training
and testing data. The test_size being 0.2 meaning, 20% of data is for validation
while 80% of the data will be trained.
10.
11. More data is generated using the training set by applying
transformations. It is required if the training set is not sufficient
enough to learn representation. The image data is generated by
transforming the actual training images by rotation, crop, shifts,
shear, zoom, flip, reflection, normalization etc.
Data Augmentation
12. Mini-Exception Model for Emotion Recognition
The architecture of the Mini_Exception
model consists of:
● Two convolutional layers followed
by batch normalization
● Then one batch is again treated
with a convolutional layer and other
batch is treated by seperable-
convolutional layer.
● The layers are followed my max
pooling and global average pooling
finalized by the softmax algorithm.
13. Convolutional 2D
The 2D convolution is a fairly simple operation at heart: you start with a kernel, which
is simply a small matrix of weights. This kernel “slides” over the 2D input data,
performing an elementwise multiplication with the part of the input it is currently on,
and then summing up the results into a single output pixel.
14. Batch Normalization and Max Pooling 2D
Batch normalization reduces the amount by what the hidden unit values shift around
(covariance shift).
In case of Max Pooling, a kernel of size n*n is moved across the matrix and for each
position the max value is taken and put in the corresponding position of the output
matrix.
15. Global Average Pooling
(GAP) layers to minimize overfitting by reducing the total number of parameters in
the model. Similar to max pooling layers, GAP layers are used to reduce the spatial
dimensions of a three-dimensional tensor.
16. Optimizer, Loss function and Metrics.
● Loss function used is categorical crossentropy.
● Loss function simply measures the absolute difference between our prediction
and the actual value.
● Cross-entropy loss, or log loss, measures the performance of a classification model
whose output is a probability value between 0 and 1. Cross-entropy loss increases
as the predicted probability diverges from the actual label. So predicting a
probability of .012 when the actual observation label is 1 would be bad and result
in a high loss value.
17. ● Optimizer used is the Adam() optimizer.
● Adam stands for Adaptive Moment Estimation. Adaptive
Moment Estimation (Adam) is another method that
computes adaptive learning rates for each parameter. In
addition to storing an exponentially decaying average of
past squared gradients like AdaDelta ,Adam also keeps
an exponentially decaying average of past gradients M(t),
similar to momentum:
18. Validation
● In the validation phase, various OpenCV functions and Keras functions have been
used.
● Initially the video frame is stored in a video object.
● An LBP cascade classifier is used to detect facial region of interest
● The image frame is converted into grayscale and resized and reshaped with the
help of numpy.
● This resized image is fed to the predictor which is loaded by
keras.models.load_model() function.
● The max argument is output.
● A rectangle is drawn around the facial regions and the output is formatted above
the rectangular box.
19. Performance Evaluation
The model gives 65-66% accuracy on validation set while training the model. The CNN
model learns the representation features of emotions from the training images. Below
are few epochs of training process with batch size of 64.