Affective Computing
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Affective computing is the study of human emotion through artificial intelligence. It aims to build machines that can recognize and simulate human affect. The document discusses theories of emotion, ways to detect emotions like facial expressions and speech, and enabling technologies like sensors and machine learning. It provides examples of current affective computing applications in retail, medical, education, and social contexts. Areas being developed include affective wearables and scenarios where emotion recognition could help emergency response or improve human-computer interaction.
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Affective computing
This document provides an overview of affective computing. It begins with definitions of affective computing and discusses its goals of allowing computers to recognize and express human emotions. Applications are described across various domains like education, banking, healthcare. Research areas like emotion detection from facial expressions, speech and other signals are outlined. Issues around privacy, misuse and effectiveness are noted. The document concludes that context awareness, multimodal detection and real-time processing will be important future research directions.
Affective computing
Affective computing is the study and development of systems that can recognize, interpret, and simulate human emotions. The goal is to develop "emotion aware" machines that can detect human emotion through facial expressions, speech, gestures, etc. and respond appropriately. Some applications include e-learning systems that adapt based on a user's emotional state, and e-therapy systems that can evaluate a patient's psychological state from their real-world behaviors and expressions. Affective computing draws from psychological theories of emotion to interpret emotional cues and responses.
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Affective computing
This document provides an overview of affective computing. It begins with definitions of affective computing and discusses its goals of allowing computers to recognize and express human emotions. Applications are described across various domains like education, banking, healthcare. Research areas like emotion detection from facial expressions, speech and other signals are outlined. Issues around privacy, misuse and effectiveness are noted. The document concludes that context awareness, multimodal detection and real-time processing will be important future research directions.
Affective computing
Affective computing is the study and development of systems that can recognize, interpret, and simulate human emotions. The goal is to develop "emotion aware" machines that can detect human emotion through facial expressions, speech, gestures, etc. and respond appropriately. Some applications include e-learning systems that adapt based on a user's emotional state, and e-therapy systems that can evaluate a patient's psychological state from their real-world behaviors and expressions. Affective computing draws from psychological theories of emotion to interpret emotional cues and responses.
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The vast majority of research has used actors performing prototypical emotions like anger, sadness, joy, fear, and disgust (“read the alphabet angrily!”). In real-life, emotions are less extreme and more mixed. Studies of natural speech are still a bit hard to come by, but have been increasing in recent years. We’ll focus on these methods and results, which transfer more easily to real-world applications.
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Facial expression recognition projc 2 (3) (1)
DETECTING EMOTION FROM FACIAL EXPRESSION HAS BECOME AN URGENT NEED BECAUSE OF
ITS IMMENSE APPLICATIONS IN ARTIFICIAL INTELLIGENCE SUCH AS HUMAN-COMPUTER
COLLABORATION, DATA DRIVEN ANIMATION, HUMAN-ROBOT COMMUNICATION ETC. SINCE IT
IS A DEMANDING AND INTERESTING PROBLEM IN COMPUTER VISION, SEVERAL WORKS HAD
BEEN CONDUCTED REGARDING THIS TOPIC. THE OBJECTIVE OF THIS PROJECT IS TO DEVELOP A
FACIAL EXPRESSION RECOGNITION SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
WITH DATA AUGMENTATION. THIS APPROACH ENABLES TO CLASSIFY SEVEN BASIC EMOTIONS
CONSIST OF ANGRY, DISGUST, FEAR, HAPPY, NEUTRAL, SAD AND SURPRISE FROM IMAGE DATA.
CONVOLUTIONAL NEURAL NETWORK WITH DATA AUGMENTATION LEADS TO HIGHER
VALIDATION ACCURACY THAN THE OTHER EXISTING MODELS (WHICH IS 96.24%) AS WELL AS
HELPS TO OVERCOME THEIR LIMITATIONS.
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In this presentation, you’ll learn how computational linguists, phoneticians, and psychologists have approached emotion detection. You’ll learn about measurements and get a summary of the cues that seem to matter most.
The vast majority of research has used actors performing prototypical emotions like anger, sadness, joy, fear, and disgust (“read the alphabet angrily!”). In real-life, emotions are less extreme and more mixed. Studies of natural speech are still a bit hard to come by, but have been increasing in recent years. We’ll focus on these methods and results, which transfer more easily to real-world applications.
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Facial expression recognition projc 2 (3) (1)
DETECTING EMOTION FROM FACIAL EXPRESSION HAS BECOME AN URGENT NEED BECAUSE OF
ITS IMMENSE APPLICATIONS IN ARTIFICIAL INTELLIGENCE SUCH AS HUMAN-COMPUTER
COLLABORATION, DATA DRIVEN ANIMATION, HUMAN-ROBOT COMMUNICATION ETC. SINCE IT
IS A DEMANDING AND INTERESTING PROBLEM IN COMPUTER VISION, SEVERAL WORKS HAD
BEEN CONDUCTED REGARDING THIS TOPIC. THE OBJECTIVE OF THIS PROJECT IS TO DEVELOP A
FACIAL EXPRESSION RECOGNITION SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
WITH DATA AUGMENTATION. THIS APPROACH ENABLES TO CLASSIFY SEVEN BASIC EMOTIONS
CONSIST OF ANGRY, DISGUST, FEAR, HAPPY, NEUTRAL, SAD AND SURPRISE FROM IMAGE DATA.
CONVOLUTIONAL NEURAL NETWORK WITH DATA AUGMENTATION LEADS TO HIGHER
VALIDATION ACCURACY THAN THE OTHER EXISTING MODELS (WHICH IS 96.24%) AS WELL AS
HELPS TO OVERCOME THEIR LIMITATIONS.
Hand Gesture Recognition Applications
This project developed a gesture recognition application using machine learning algorithms. The application recognizes gestures without color markers by extracting features from images using Hu moments and training a Hidden Markov Model. Common gestures like "ok" and "peace" were mapped to tasks like switching slides. The system was tested and achieved 60% accuracy. Future work could involve adding more gestures and connecting it to other devices.
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The document summarizes an OpenCV based image processing attendance system. It discusses using OpenCV to detect faces in images and recognize faces by comparing features to a database. The key steps are face detection using Viola-Jones detection, face recognition using eigenfaces generated by principal component analysis to project faces into "face space", and measuring similarity by distance between projections.
Emotion recognition using image processing in deep learning
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.
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This document proposes a framework for dynamic hand gesture recognition using key frame extraction. The framework uses skin color segmentation to detect the hand in video frames. Key frames are then extracted from the video using an algorithm to identify important distinguishing frames. Features related to hand shape, motion, and orientation are extracted from the key frames. A multi-class support vector machine classifier is used to classify the gestures based on the extracted features. The framework achieves 90.46% accuracy in recognizing 22 dynamic hand gestures of Indian sign language based on experiments.
Chapter two (1)hci
The document describes the human processor model (MHP) which simplifies human processing into three subsystems - perceptual, motor, and cognitive. Each subsystem contains a processor and memory. The MHP also includes principles of operation that dictate behavior under certain conditions. The model involves input from senses like vision, hearing, and touch which are processed and output through effectors like fingers, voice, eyes, and body position. Memory is divided into sensory buffers, short-term memory, and long-term memory. Thinking involves reasoning through deduction, induction, and abduction as well as problem solving using various approaches. Emotion and individual differences also impact human processing.
Emotion detection using cnn.pptx
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.
Interfaces to ubiquitous computing
1. Ubiquitous computing interfaces must consider a broader range of inputs beyond traditional keyboards and mice to include human motion, activity, preferences and desired actions.
2. Interaction design for ubiquitous interfaces depends on factors like complexity, novelty, stability and cost of the proposed system. User-centered design, systems design, and genius design are methodologies used.
3. New classes of interfaces have emerged like tangible interfaces, surface interfaces and ambient interfaces to accommodate new input technologies like gestures, speech, and ambient feedback beyond traditional graphical user interfaces.
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2017: Transformative Technology Overview
This deck gives an 2017 quick overview of Transformative Technology , i.e. tech to support mental health, emotional wellbeing, and human thriving.
Mind reading
Mind reading technology uses functional near-infrared spectroscopy (fNIRS) to measure blood volume and oxygen levels in the brain. A headband sends light into the head tissues and measures light absorption to gauge metabolic brain activity. The computer then analyzes facial expressions and head gestures in real-time to infer mental states like emotions and thoughts. While this technology could help paralyzed patients or those who cannot speak, it also risks privacy breaches and potential misuse if developed without proper safeguards.
Blue eyes technology.pptx
This document discusses various technologies for creating more human-like computer interactions, including emotion recognition, eye tracking, speech recognition, and interest tracking. It aims to give computers perceptual and sensory abilities similar to humans. Technologies discussed include using cameras and microphones for non-obtrusive emotion sensing, an emotion mouse that tracks pressure, skin responses and heart rate, facial expression recognition, eye tracking to follow gaze, and speech input. The goal is to make computers more interactive partners that can understand a user's physical and emotional states.
Artificial Intelligence vs. Machine Learning
John McCarthy first coined the term "artificial intelligence" in 1956. The original concept of AI was for machines to simulate human learning and intelligence. There are two main types of AI - strong AI, which aims to simulate the human brain, and weak AI, which behaves intelligently without replicating the brain. Machine learning is a subset of AI that uses algorithms to improve performance over time by processing data. AI is now used widely in areas like digital assistants, social media, music/video streaming, navigation, business applications, drones, self-driving cars, and humanoid robots. While AI has benefits, there are also risks like limited abilities, unemployment, and autonomous weapons. The future of AI could include enhanced human abilities but
Intro to Vita Beans
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PPT presentation on ARTIFICIAL INTELLIGENCE
The document presents an overview of artificial intelligence (AI) created by a group of students. It defines AI as the simulation of human intelligence by machines. The document then discusses the history of AI, current applications including healthcare, education, and aviation, as well as goals, approaches, tools, platforms, advantages, and disadvantages. It notes that while AI can perform tasks efficiently with little error, it may also decrease human labor and pose ethical issues. The future of AI is discussed, with predictions that speech and image recognition will improve human-device interaction and AI will start to operate more autonomously.
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facial Emotion Datection using Artificial intelligence
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Affective Computing
- 1. AFFECTIVE COMPUTING (The Power of Emotion Analytics) Presented By, Debabrata Chakraborty,
- 2. Affective Computing What is it and why should I care?
- 3. AFFECTIVE COMPUTING? • Human Side of AI (Artificial Intelligence)
- 4. Emotions and its Theories! Emotions? • Feelings • Mood & affect Theories Of Emotions • James-Lange • Canon-Bard • Schacter-Singer • Lazarus
- 5. Theories Of Emotions Theories Of Emotions : James-Lange Event Physiological Response Interpretation Of Emotion
- 6. Theories Of Emotions : Canon-Bard Physiological Response Event Emotion
- 7. Theories Of Emotions : Schacter-Singer Event Physiological Response Identity the reason for Emotion
- 8. Theories Of Emotions : Lazarus Emotion Event Appraise(Label) Physiological Response
- 9. Ways Of Detecting Emotions! • Facial Expression • Through Speech • Physiological Based
- 10. Affective Computing! The Beginning: • Rosalind Picard • MIT Media Lab
- 11. Emotions in Machines • Senses emotional state of the user • Sensor , camera , microphone • Speech Recognition • Gesture Recognition • Image Recognition • Machine Learning
- 12. Enabling Technologies • Sensor Technology • Big Data • Machine Learning • API & Services
- 13. Microsoft Emotion API • Image as input • Emotions : 1. Happiness 2. Sadness 3. Anger 4. Fear 5. Contempt 6. Disgust 7. Neutral 8. Surprise
- 14. IBM Watson Tone Analyser • Text as Input • Emotions : 1. Analytical 2. Anger 3. Confident 4. Fear 5. Tentative
- 15. Application Scenario's Application Scenario's : Retail • Shops equipped with Cameras • Registering Customers Interaction • Enhanced Customer Service Medical Retail Educationa l Social
- 16. Application Scenario's : Medical • Measure the emotion of patient by Physiological measures • Medical Team Responds better to the Needs of the patient Medical Retail Educationa l Social
- 17. Application Scenario's : Education • Students Facial Expression Monitored • Teacher adjust teaching style accordingly • Both IRL and Online Medical Retail Educationa l Social
- 18. Application Scenario's : Social • Growing elderly population • Robots Socializing • Combination of facial recognition, Speech evaluation and sociological measures Medical Retail Educationa l Social
- 19. What is in the frontline? Affectiva : • Emotion DB • Emotion SDK and API • Virtual Assistance • Personalized transportation experiences • Advertising : Understanding Emotional Management
- 20. Pepper : • Voice Recognition • Face Recognition • Body Movements • Use emotion recognition API from Affectiva • Eye Colour , Tablet , Tone of voice • Bank • Nestle Nescafe
- 21. AREAS OF AFFECTIVE COMPUTING AFFECTIVE WEARABLES • Sensors & tools can be used in recognizing affective patterns, • but these tools require a lot of attention/ maintenance. Figure : Wearer’s Blood Volume Figure : Sample & transmit biometric data to Pressure larger computer for analysis
- 22. Some Scenarios -“we had an accident. Please come fast. "Sensing the stress in his voice, the computer replied with a calm, soothing voice -“Sir, please calm down, we are on our way “ and dispatched emergency forces to the scene
- 23. Scenarios
- 24. Conclusion Affective Computing is a young field of research • something far better than the current crop of “intelligent” •improving the quality of life in impaired people •research into the human brain/mind and so on.
- 25. References • R.W. Picard (1995), "Affective Computing“,MIT Media Lab. • IBM presentation on Affective Computing.(https://www.slideshare.net/diannepatricia/affective- computing-short-external) • https://www.slideshare.net/Rishikese/emotion-based-computing • Håkan Silfvernagel on Affective Computing.(https://youtu.be/SLbiSXU8VJk)