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This document provides an overview of gesture recognition technology, including what gestures are, the history and basic workings of gesture recognition, different types of gesture recognition and sensing technologies, algorithms used, applications, and challenges. It discusses hand, facial, and sign language recognition and technologies like wired gloves, cameras, and controllers. Benefits include interacting without mouse/keyboard and with 3D environments without physical contact. Applications include rehabilitation, sign language, gaming, and assisting those with disabilities.
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Gesture Recognition
Gesture recognition technology uses cameras to read human body movements and gestures as a form of input to control devices and applications. A camera captures gestures like hand movements and facial expressions and sends that data to a computer for interpretation. Gesture recognition allows humans to interact with machines naturally without physical devices by using gestures to control cursors, activate menus, or control games and other applications. There are different methods for capturing and interpreting gestures including using wired gloves, depth cameras, stereo cameras, single cameras, or motion controllers.
Gesture recognition technology
This document discusses gesture recognition. It begins by introducing gesture recognition and its evolution from graphical user interfaces using mice and keyboards. It then defines different types of gestures including iconic, deictic, metaphoric, and beat gestures. The document outlines the basic working of a gesture recognition system and different types of gesture sensing technologies like hand gesture recognition, facial gesture recognition, sign language recognition, and vision-based techniques. It discusses input devices used for gesture tracking and various applications of gesture recognition like socially assistive robotics, sign language translation, virtual controllers, and remote control. Finally, it addresses challenges in gesture recognition like lack of a universal gesture language and issues with robustness.
Part 1 - Gesture Recognition Technology
Gesture recognition technology allows humans to interface with computers using body movements detected by cameras. Cameras read gestures like hand movements and send that data to computers, which can then use the gestures for control or input. Current research focuses on emotion recognition from facial expressions and interpreting sign language through computer vision algorithms. Gesture recognition has applications in assistive robotics, gaming, alternative interfaces, and more.
Gesture recognition technology
This document summarizes a presentation on gesture recognition technology. It discusses the introduction of gesture recognition, the types of gestures, uses of gesture recognition including sign language recognition and virtual controllers. It also discusses input devices such as wired gloves and depth cameras. The document outlines algorithms for gesture recognition including 3D model-based, skeletal-based, and appearance-based algorithms. It concludes with discussion of challenges for gesture recognition including limitations of equipment and variations in recognition accuracy.
Part 2 - Gesture Recognition Technology
Gesture recognition technology allows users to control devices through natural hand motions and gestures instead of physical interfaces like buttons. The document discusses how gesture recognition could be used for smart home controls, education applications, automotive controls, and medical and entertainment devices. It also mentions some companies working on gesture recognition technology, including Apple, Intel, and an Israeli startup called Omek that was acquired by Intel.
Real time gesture recognition
This document presents a real-time hand gesture recognition method. It discusses algorithms like 3D model-based, skeletal-based, and appearance-based for hand gesture recognition. The process involves hand detection, tracking, segmentation, and recognition. Features, advantages, and applications are also covered. The method uses fast hand tracking, segmentation, and multi-scale feature extraction for accurate recognition. It concludes with discussing potential for continued progress in areas like sign language recognition and accessibility.
Gesture recognition technology
The document discusses gesture recognition technology. It describes how cameras can read human body movements and communicate that data to computers to interpret gestures. Gestures can be used as inputs to control devices or applications. The document outlines different types of gestures, image processing techniques used, input devices like gloves and cameras, challenges, and potential uses like sign language recognition and immersive gaming.
GESTURE RECOGNITION TECHNOLOGY
Gesture recognition technology allows for control of devices through hand and body motions. It works by using cameras, sensors and algorithms to interpret gestures and movements. Key applications include controlling smart TVs with hand motions, sign language translation, and assisting disabled individuals. Challenges include variations between individuals, reading motions accurately due to lighting and noise, and lack of standardized gesture languages.
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Gesture Recognition
Gesture recognition technology uses cameras to read human body movements and gestures as a form of input to control devices and applications. A camera captures gestures like hand movements and facial expressions and sends that data to a computer for interpretation. Gesture recognition allows humans to interact with machines naturally without physical devices by using gestures to control cursors, activate menus, or control games and other applications. There are different methods for capturing and interpreting gestures including using wired gloves, depth cameras, stereo cameras, single cameras, or motion controllers.
Gesture recognition technology
This document discusses gesture recognition. It begins by introducing gesture recognition and its evolution from graphical user interfaces using mice and keyboards. It then defines different types of gestures including iconic, deictic, metaphoric, and beat gestures. The document outlines the basic working of a gesture recognition system and different types of gesture sensing technologies like hand gesture recognition, facial gesture recognition, sign language recognition, and vision-based techniques. It discusses input devices used for gesture tracking and various applications of gesture recognition like socially assistive robotics, sign language translation, virtual controllers, and remote control. Finally, it addresses challenges in gesture recognition like lack of a universal gesture language and issues with robustness.
Part 1 - Gesture Recognition Technology
Gesture recognition technology allows humans to interface with computers using body movements detected by cameras. Cameras read gestures like hand movements and send that data to computers, which can then use the gestures for control or input. Current research focuses on emotion recognition from facial expressions and interpreting sign language through computer vision algorithms. Gesture recognition has applications in assistive robotics, gaming, alternative interfaces, and more.
Gesture recognition technology
This document summarizes a presentation on gesture recognition technology. It discusses the introduction of gesture recognition, the types of gestures, uses of gesture recognition including sign language recognition and virtual controllers. It also discusses input devices such as wired gloves and depth cameras. The document outlines algorithms for gesture recognition including 3D model-based, skeletal-based, and appearance-based algorithms. It concludes with discussion of challenges for gesture recognition including limitations of equipment and variations in recognition accuracy.
Part 2 - Gesture Recognition Technology
Gesture recognition technology allows users to control devices through natural hand motions and gestures instead of physical interfaces like buttons. The document discusses how gesture recognition could be used for smart home controls, education applications, automotive controls, and medical and entertainment devices. It also mentions some companies working on gesture recognition technology, including Apple, Intel, and an Israeli startup called Omek that was acquired by Intel.
Real time gesture recognition
This document presents a real-time hand gesture recognition method. It discusses algorithms like 3D model-based, skeletal-based, and appearance-based for hand gesture recognition. The process involves hand detection, tracking, segmentation, and recognition. Features, advantages, and applications are also covered. The method uses fast hand tracking, segmentation, and multi-scale feature extraction for accurate recognition. It concludes with discussing potential for continued progress in areas like sign language recognition and accessibility.
Gesture recognition technology
The document discusses gesture recognition technology. It describes how cameras can read human body movements and communicate that data to computers to interpret gestures. Gestures can be used as inputs to control devices or applications. The document outlines different types of gestures, image processing techniques used, input devices like gloves and cameras, challenges, and potential uses like sign language recognition and immersive gaming.
GESTURE RECOGNITION TECHNOLOGY
Gesture recognition technology allows for control of devices through hand and body motions. It works by using cameras, sensors and algorithms to interpret gestures and movements. Key applications include controlling smart TVs with hand motions, sign language translation, and assisting disabled individuals. Challenges include variations between individuals, reading motions accurately due to lighting and noise, and lack of standardized gesture languages.
Gesture Technology
Gesture recognition technology uses mathematical algorithms to interpret human gestures and enable interaction with machines without physical devices. It has various applications including sign language recognition, interpreting facial expressions, and electrical field sensing of body proximity. Vision-based and device-based techniques use cameras, gloves, or other sensors to detect gestures. Challenges include varying lighting and background items that can reduce accuracy. The future potential is vast across entertainment, home automation, education, medicine and security.
Gesture recognition adi
This document provides an overview of gesture recognition systems. It describes the basic architecture, which involves an input device sending gestures to a computer for processing and recognition. Common input devices include data gloves and cameras. The benefits of gesture recognition are that it provides a more natural human-computer interface without physical devices. Applications include interacting with virtual environments, robots, and public displays. Challenges to accurate recognition include lighting, camera quality, and background noise.
Finalgesture22
This document discusses gesture recognition, including what gestures are, types of gesture recognition like facial, hand, and sign language recognition. It covers the basic working of gesture technology and types of gesture sensing technologies such as device, electrical field, and vision-based sensing. Some applications of gesture recognition discussed include controlling devices, sign language translation, and assisting with patient rehabilitation. Challenges to gesture recognition are also mentioned such as lack of standard gesture languages and issues with robustness due to lighting and noise factors.
Gesture Recognition Technology
Gesture recognition is a topic in computer science and language technology which interpret human gestures via mathematical algorithms.
Gestures can originate from any bodily motion or state but commonly originate from the face or hand.
Gesture recognition enables humans to communicate with the machine (HMI) and interact naturally without any mechanical devices.
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Gestures are an important form of non-verbal communication that involve visible bodily motions. The document discusses the history and development of gesture recognition technologies, describing early data gloves and videoplace systems as well as current technologies like Cepal and ADITI that help people with disabilities control devices with gestures. It also outlines the key components of a gesture recognition system including modeling, analysis, and recognition of gestures and discusses classification methods like HMMs and MLPs. Applications discussed include virtual keyboards, navigaze, and Sixth Sense technology.
GESTURE prestation
This document discusses gesture recognition. It defines a gesture as a form of non-verbal communication using bodily movements. The document then provides examples of gestures and discusses how gesture recognition works by using computer vision and image processing techniques. It outlines different types of gestures including hand gestures, sign language, and gestures detected using electrical fields. The document discusses advantages such as more natural human-computer interaction and disadvantages including issues with ambient light and object detection. It concludes by discussing future trends in gesture recognition technology.
Gesture recognition techniques
Gesture Recognition Techniques, Leap Motion Sensor: Taking leap to control anything in a real human manner unlike traditional artificial taps and clicks , Sviacam, Eviacam, moves the mouse pointer as you move your Head.
Gesture recognition using artificial neural network,a technology for identify...
The presentation contains a technology for identifying any type of body motions commonly originating from hand and face using artificial neural network.This include identifying sign language also.This technology is for speech impaired individuals.
// I have shared an IJCSE standard paper in this topic
Gesture recognition technology
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gesture-recognition
This document is a final report on gesture recognition submitted by three students. It contains an abstract, introduction, background information on gesture recognition including American Sign Language and object recognition techniques. It discusses digital image processing and neural networks. It outlines the approach, modules, flowcharts, results and conclusions of the project, which developed a method to recognize static hand gestures using a perceptron neural network trained on orientation histograms of the input images. Source code and applications are also discussed.
Gesture recognition document
Gesture recognition is the process of understanding and interpreting meaningful movements of the hands, arms, face, or sometimes head
Mobile Device For Electronic Eye Gesture Recognition
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Gestures are expressive, meaningful body motions, i.e., physical movements of the fingers, hands, arms, head, face, or body with the intent to convey information or interact with the environment.
Gesture recognition
The Gesture Recognition Technology is rapidly growing technology and this PPT describes about the working of gesture recognition technology,the sub fields in it, its applications and the challenges it faces.
Gesture recognition
Gesture recognition is the process of understanding and
interpreting meaningful movements of the hands, arms, face,
or sometimes head. It is of great need in designing an
efficient human-computer interface. The technology has
been in study in recent years because of its potential for
application in user interfaces
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This document discusses gesture recognition technology and its potential applications in retail, known as Retail 2.0. It covers the basics of gesture recognition technology, how it can be used for retail advertising and market research applications, potential uses like emotion recognition and observing consumer behavior, and challenges like cost, accuracy, and ensuring the technology maintains its "wow effect" over time. The document argues that gesture recognition could enhance retail experiences and provide valuable consumer insights through interactive digital displays.
Gesture Recognition
This document provides an introduction and overview of hand gesture recognition. It discusses what gestures are, how gesture recognition works to interpret human body language and enable natural human-computer interaction. It outlines the key modules involved, including image transformation techniques like frame extraction, blurring and color thresholding. Example hand gestures and applications are shown, along with the overall data flow and required hardware and software components.
Gesture Recogntion Technology
this slide lets you know about all the information you need to know about the gesture recognition technology and their advancements.
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Gesture recognition allows humans to interface with computers using bodily movements, especially hand gestures. The system first acquires an image, preprocesses it through steps like segmentation and filtering, then extracts features using edge detection. It matches the extracted features to a database of signatures for known gestures. The system was tested on 25 basic American sign language gestures and achieved 98.6% accuracy in recognizing 493 out of 500 gestures. Challenges include inconsistent lighting and background noise.
Hand gesture recognition system for human computer interaction using contour ...
This document describes a hand gesture recognition system that allows users to control computer operations using hand gestures captured by a webcam. The system involves four main phases: 1) image acquisition using a webcam, 2) image pre-processing to extract the hand and reduce noise, 3) feature extraction by detecting hand contours, and 4) gesture recognition by comparing contour features to stored templates and assigning computer commands. The system was able to recognize various gestures like opening programs or pressing keys with an average recognition rate of 95%. Future work could involve reducing constraints on the user environment and allowing both hands to perform more operations.
Gesture Recognition Technology-Seminar PPT
This document provides an overview of gesture recognition technology. It begins with introducing gestures as a form of non-verbal communication and defines gesture recognition as interpreting human gestures through mathematical algorithms. It then discusses the motivation for gesture recognition, including its naturalness and applications in overcoming interaction problems with traditional input devices. The document outlines different types of gestures, input devices like gloves and cameras, challenges like developing standardized gesture languages, and uses like sign language recognition, virtual controllers, and assisting disabled individuals. It concludes with references for further reading.
ppt of gesture recognition
Gestures are an important form of non-verbal communication between humans and can also be used to create interfaces between humans and machines. There are several types of gestures including emblems, sign languages, gesticulation and pantomimes. Gesture recognition allows humans to interact with computers through motions of the body, especially hand movements. Some methods of gesture recognition include device-based techniques using sensors on gloves, vision-based techniques using cameras, and controller-based techniques using motion controllers. Gesture recognition has applications in areas such as virtual controllers, sign language translation, game interaction and robotic assistance.
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Gesture recognition technology uses mathematical algorithms to interpret human gestures and enable interaction with machines without physical devices. It has various applications including sign language recognition, interpreting facial expressions, and electrical field sensing of body proximity. Vision-based and device-based techniques use cameras, gloves, or other sensors to detect gestures. Challenges include varying lighting and background items that can reduce accuracy. The future potential is vast across entertainment, home automation, education, medicine and security.
Gesture recognition adi
This document provides an overview of gesture recognition systems. It describes the basic architecture, which involves an input device sending gestures to a computer for processing and recognition. Common input devices include data gloves and cameras. The benefits of gesture recognition are that it provides a more natural human-computer interface without physical devices. Applications include interacting with virtual environments, robots, and public displays. Challenges to accurate recognition include lighting, camera quality, and background noise.
Finalgesture22
This document discusses gesture recognition, including what gestures are, types of gesture recognition like facial, hand, and sign language recognition. It covers the basic working of gesture technology and types of gesture sensing technologies such as device, electrical field, and vision-based sensing. Some applications of gesture recognition discussed include controlling devices, sign language translation, and assisting with patient rehabilitation. Challenges to gesture recognition are also mentioned such as lack of standard gesture languages and issues with robustness due to lighting and noise factors.
Gesture Recognition Technology
Gesture recognition is a topic in computer science and language technology which interpret human gestures via mathematical algorithms.
Gestures can originate from any bodily motion or state but commonly originate from the face or hand.
Gesture recognition enables humans to communicate with the machine (HMI) and interact naturally without any mechanical devices.
Nikppt
Gestures are an important form of non-verbal communication that involve visible bodily motions. The document discusses the history and development of gesture recognition technologies, describing early data gloves and videoplace systems as well as current technologies like Cepal and ADITI that help people with disabilities control devices with gestures. It also outlines the key components of a gesture recognition system including modeling, analysis, and recognition of gestures and discusses classification methods like HMMs and MLPs. Applications discussed include virtual keyboards, navigaze, and Sixth Sense technology.
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This document discusses gesture recognition. It defines a gesture as a form of non-verbal communication using bodily movements. The document then provides examples of gestures and discusses how gesture recognition works by using computer vision and image processing techniques. It outlines different types of gestures including hand gestures, sign language, and gestures detected using electrical fields. The document discusses advantages such as more natural human-computer interaction and disadvantages including issues with ambient light and object detection. It concludes by discussing future trends in gesture recognition technology.
Gesture recognition techniques
Gesture Recognition Techniques, Leap Motion Sensor: Taking leap to control anything in a real human manner unlike traditional artificial taps and clicks , Sviacam, Eviacam, moves the mouse pointer as you move your Head.
Gesture recognition using artificial neural network,a technology for identify...
The presentation contains a technology for identifying any type of body motions commonly originating from hand and face using artificial neural network.This include identifying sign language also.This technology is for speech impaired individuals.
// I have shared an IJCSE standard paper in this topic
Gesture recognition technology
It is the best and attractive ppt of Gesture Recognition Technology...This is the TOUCHLESS technology...and will surely hit the market...in coming days.
gesture-recognition
This document is a final report on gesture recognition submitted by three students. It contains an abstract, introduction, background information on gesture recognition including American Sign Language and object recognition techniques. It discusses digital image processing and neural networks. It outlines the approach, modules, flowcharts, results and conclusions of the project, which developed a method to recognize static hand gestures using a perceptron neural network trained on orientation histograms of the input images. Source code and applications are also discussed.
Gesture recognition document
Gesture recognition is the process of understanding and interpreting meaningful movements of the hands, arms, face, or sometimes head
Mobile Device For Electronic Eye Gesture Recognition
A mobile device that is useful for special kind of people to control the home appliances only with their eye gestures.
Gesture recognition PPPT
Gestures are expressive, meaningful body motions, i.e., physical movements of the fingers, hands, arms, head, face, or body with the intent to convey information or interact with the environment.
Gesture recognition
The Gesture Recognition Technology is rapidly growing technology and this PPT describes about the working of gesture recognition technology,the sub fields in it, its applications and the challenges it faces.
Gesture recognition
Gesture recognition is the process of understanding and
interpreting meaningful movements of the hands, arms, face,
or sometimes head. It is of great need in designing an
efficient human-computer interface. The technology has
been in study in recent years because of its potential for
application in user interfaces
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This document discusses gesture recognition technology and its potential applications in retail, known as Retail 2.0. It covers the basics of gesture recognition technology, how it can be used for retail advertising and market research applications, potential uses like emotion recognition and observing consumer behavior, and challenges like cost, accuracy, and ensuring the technology maintains its "wow effect" over time. The document argues that gesture recognition could enhance retail experiences and provide valuable consumer insights through interactive digital displays.
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This document provides an introduction and overview of hand gesture recognition. It discusses what gestures are, how gesture recognition works to interpret human body language and enable natural human-computer interaction. It outlines the key modules involved, including image transformation techniques like frame extraction, blurring and color thresholding. Example hand gestures and applications are shown, along with the overall data flow and required hardware and software components.
Gesture Recogntion Technology
this slide lets you know about all the information you need to know about the gesture recognition technology and their advancements.
Hand Gesture recognition
Gesture recognition allows humans to interface with computers using bodily movements, especially hand gestures. The system first acquires an image, preprocesses it through steps like segmentation and filtering, then extracts features using edge detection. It matches the extracted features to a database of signatures for known gestures. The system was tested on 25 basic American sign language gestures and achieved 98.6% accuracy in recognizing 493 out of 500 gestures. Challenges include inconsistent lighting and background noise.
Hand gesture recognition system for human computer interaction using contour ...
This document describes a hand gesture recognition system that allows users to control computer operations using hand gestures captured by a webcam. The system involves four main phases: 1) image acquisition using a webcam, 2) image pre-processing to extract the hand and reduce noise, 3) feature extraction by detecting hand contours, and 4) gesture recognition by comparing contour features to stored templates and assigning computer commands. The system was able to recognize various gestures like opening programs or pressing keys with an average recognition rate of 95%. Future work could involve reducing constraints on the user environment and allowing both hands to perform more operations.
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This document provides an overview of gesture recognition technology. It begins with introducing gestures as a form of non-verbal communication and defines gesture recognition as interpreting human gestures through mathematical algorithms. It then discusses the motivation for gesture recognition, including its naturalness and applications in overcoming interaction problems with traditional input devices. The document outlines different types of gestures, input devices like gloves and cameras, challenges like developing standardized gesture languages, and uses like sign language recognition, virtual controllers, and assisting disabled individuals. It concludes with references for further reading.
ppt of gesture recognition
Gestures are an important form of non-verbal communication between humans and can also be used to create interfaces between humans and machines. There are several types of gestures including emblems, sign languages, gesticulation and pantomimes. Gesture recognition allows humans to interact with computers through motions of the body, especially hand movements. Some methods of gesture recognition include device-based techniques using sensors on gloves, vision-based techniques using cameras, and controller-based techniques using motion controllers. Gesture recognition has applications in areas such as virtual controllers, sign language translation, game interaction and robotic assistance.
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Human Computer Interaction, Gesture provides a way for computers to understand human body language, Deals with the goal of interpreting hand gestures via mathematical algorithms, Enables humans to interface with the machine (HMI) and interact naturally without any mechanical devices
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Gestures are an important form of non-verbal communication that involve visible bodily motions. They can be used to control devices through gesture recognition systems. Such systems work by modeling, analyzing, and recognizing gestures based on features extracted from images of body movements. Various technologies have been developed to recognize both static and dynamic gestures through methods like contour analysis, Hidden Markov models, and others. Gesture recognition has applications in areas like human-computer interaction, rehabilitation robotics, and interactive gaming.
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This document discusses various types of biometrics that can be used for authentication, including hard biometrics like fingerprints, soft biometrics like build and mannerisms, and behavioral biometrics like gait and typing patterns. It notes that biometrics can be classified based on what is analyzed, such as frequency for speech or speed and pressure for signatures. The document also discusses common biometric sensors, methodologies for analyzing sensor data, and the benefits and disadvantages of different biometric factors and sensors. It promotes the idea that continuous authentication using multiple biometrics can provide more secure access than single-factor authentication.
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Sixth Sense technology is a wearable gestural interface developed by Pranav Mistry that augments the physical world with digital information. It consists of a camera, projector, and smartphone that allows hand gestures to be used to interact with and manipulate information projected onto surfaces. Some applications include making calls, getting maps and directions, checking the time, enhancing reading with videos and reviews, and taking pictures. The technology provides advantages such as portability, multi-touch interaction, and connecting the digital and physical worlds. However, it also faces limitations such as image noise and high costs. Future developments could include gaming without controllers and 3D interfaces.
Gesture Based Computing
Gesture based computing uses gestures as a form of human-computer interaction. It can be used to replace mice and keyboards by allowing users to navigate interfaces and interact with 3D environments through gestures detected by cameras. Common technologies for gesture recognition include depth cameras, controllers, and single visible light cameras. Gestures can be used for applications in entertainment, gaming, communications for disabled individuals, and as an alternative computer interface.
Google project soli
Project Soli is a new technology that uses radar to enable new types of touch less interactions. The movements of gestures from a human can be captured using a radar sensor, and by detection of these gestures, some special task on a device can be done.
Zaki_Anwer Seminar PPT.pptx
This document provides an overview of various biometric identifiers for human identification. It discusses fingerprint recognition, face recognition, voice recognition, iris recognition, hand geometry, signature verification, DNA analysis, and retina recognition. For each biometric technique, it describes the basic principles, common applications, advantages, and limitations. The document aims to educate the reader about the different biometric identification methods that exist.
GRT.pptx
I am a under graduate student from tamilnadu.This is the short intro about the emerging gesture recognisation technology.It's used in many high security applications.So that,I chosen this topic.
Gesture recognition1
The document discusses gesture recognition systems, which allow humans to interact with machines through visible bodily motions or facial expressions. It describes how such systems work by capturing images of gestures via cameras, preprocessing the images, and identifying the gestures. Examples of applications mentioned include sign language recognition, game control through gestures, and using facial expressions for affective computing. Challenges with gesture recognition systems include the cost and difficulty of developing them, as each gesture must be assigned a specific command.
2019001791_Fingerprint_Authentication.pptx
This document provides information about fingerprint authentication technology. It begins by defining biometrics as automated methods of identifying or verifying a person's identity using physiological characteristics like fingerprints. Fingerprint recognition works by analyzing the unique ridge and valley patterns of fingerprints. The verification process involves enrolling a fingerprint sample in a database and then comparing subsequent samples to entries in the database. Fingerprint authentication is widely used for security systems due to the low probability of any two fingerprints being identical. Common applications include securing devices, buildings, and network access.
Facial Recognition Technology
Facial recognition technology works by measuring nodal points on faces such as the distance between the eyes and shape of cheekbones. These points are compared to a database of pictures to find a match. It is used in security systems and can identify people passing by cameras. While it provides security benefits, concerns include privacy issues with mass surveillance and systems being fooled by disguises or lighting/camera angle changes. Recent improvements have made algorithms over 100 times more accurate than in 1995 and able to outperform humans in some cases.
Shoulder Surfing as future technology
Current authentication systems have drawbacks including vulnerability to shoulder surfing attacks where passwords can be stolen by observing users type them. New authentication technologies aim to address this by using biometrics like fingerprints, voice recognition, brainwaves, or butt prints. However, shoulder surfing remains effective because it targets the human as the weakest link by easily observing passwords entered in public. To protect information, multi-factor authentication is recommended along with vigilance against hidden cameras through regular searches and detector apps.
Presentation suresh maurya
This presentation summarizes fingerprint recognition as a biometric security method. It discusses how fingerprint recognition systems work by acquiring, analyzing, and matching fingerprint patterns and features. The document outlines the types of fingerprint sensors, verification techniques involving ridge and minutia analysis, and technologies used in fingerprint matching. Current and potential applications of fingerprint recognition are mentioned, along with advantages like security and limitations regarding costs. The presentation concludes that biometrics can enhance security when multiple methods are used together.
Augmented Reality
All important aspects of AR are briefly shown in this PPT, including the different types of Augmented Reality,its applications, differences between Augmented Reality and Virtual Reality.
sixth sense technology
The document describes Sixth Sense technology, a wearable gestural interface created by Pranav Mistry. It consists of a camera, projector, mirror, mobile device, and colored markers. The camera tracks hand movements and objects, the projector augments physical environments with projected interfaces, and the mobile device acts as the processing unit. Some applications include checking the time, making calls, taking photos, and getting flight updates through gestures. While portable and low-cost, it has limitations from device hardware and requires correct gestures. The source code was released open source to further develop this technology that connects the physical and digital world through gestures.
Biomatrics
The document provides an introduction to biometrics, which are automated methods of recognizing a person based on physiological or behavioral characteristics like fingerprints, face, iris, retina, hand geometry, signature, and voice. It discusses different biometric modalities, applications, types of sensors, the biometric system process, how some biometric technologies like fingerprint and facial scanners work, barriers to biometrics adoption, and companies involved in different biometric technologies.
Comparisons of input modalities and methods
Mechanical, audio, gaze, brain, and multimodal input methods were compared. Silent speech recognition such as lip reading was highlighted as a preferred text input technology as it is robust against noise and protects privacy. Gaze tracking was described as fast for pointing tasks as it provides information on user intentions. Finger dexterity via mechanical movement was noted to be reliable for controlling and commanding machines.
Sixth sense technology
Sixth Sense is a wearable gestural interface that uses hand gestures and motions to allow users to interact with digital information projected onto any surface. It consists of a pocket projector, camera, and mobile phone that work together to project information and allow interaction through hand gestures without the need for physical devices like keyboards or mice. Some potential uses include sign language recognition, augmented reality applications, controlling devices through gestures, and creating alternative computer interfaces that make the entire world a computer interface. The goal is to make the technology widely available and adapt computers to human needs through more natural gestural interaction.
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gesture recognition!
- 1. Mehran kordavani Saeed bahrani Super vision by: mr chabi
- 2. content: • What is Gesture? • What is gesture recognition Technology? • History • Basic working of Gesture Technology • Types of Gesture Recognition • Types of Gesture Sensing Technology • Technology Behind It • BENEFITS OF GESTURE RECOGNITION • Algorithms • Uses Of Gesture Recognition • Gesture Recognition Challenges • Conclusion
- 5. What is gesture recognition Technology?
- 6. smart T.V gesture recognition software in a laptop
- 7. History:
- 8. Basic working of Gesture Technology
- 9. Types of Gesture Recognition Hand Gesture Recognition Facial Gesture Recognition Sign Language Recognition
- 10. Types of Gesture Recognition Hand Gesture Recognition
- 13. Types of Gesture Sensing Technology Electrical Field Sensing Technology Device Sensing Technology Vision Based Technologies
- 14. Electrical Field Sensing Technology
- 17. Technology Behind It Wired gloves Stereo cameras Depth-aware cameras Thermal cameras Controller –based gestures Single camera or a Proximity sensor
- 18. Wired gloves
- 19. Single camera or a Proximity sensor
- 20. Stereo cameras
- 23. Thermal cameras
- 27. BENEFITS OF GESTURE RECOGNITION Replace mouse and keyboard in computer Interact with the 3D world No physical contact with machine
- 28. Algorithms 3D model-based algorithms Skeletal based algorithms
- 30. !
- 31. Uses Of Gesture Recognition rehabilitation Sign language recognition
- 32. Virtual controllers Remote control
- 33. Aid to physically challenged Immersive gaming technology
- 34. Gesture Recognition Challenges Lack of Gesture Language Robustness
- 35. Conclusion
- 36. Refrence: • Per Malmestig, Sofie Sundberg, SignWiiver - implementation of sign language technology • Vladimir I. Pavlovic, Rajeev Sharma, Thomas S. Huang, Visual Interpretation of Hand Gestures for Human-Computer Interaction; A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997