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.

1. GESTURE TECHNOLOGY

2. INTRODUCTION
 Gesture recognition is a language technology with the goal
of interpreting 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 interface with the
machine (HMI) and interact naturally without any
mechanical devices. Using the concept of gesture
recognition, it is possible to point a finger at the computer
screen so that the cursor will move accordingly.

3. EXAMPLE
 Military air marshals use hand and body
gestures to direct flight operations aboard
aircraft carriers.

4. GESTURE TYPES
 In computer interfaces, two types of gestures are
distinguished: We consider online gestures, which can also
be regarded as direct manipulations like scaling and
rotating. In contrast, offline gestures are usually processed
after the interaction is finished; e.g. a circle is drawn to
activate a context menu.
 Offline gestures: Those gestures that are processed after
the user interaction with the object. An example is the
gesture to activate a menu.
 Online gestures: Direct manipulation gestures. They are
used to scale or rotate a tangible object.

5. A BASIC WORKING OF THE GESTURE
RECOGNITION SYSTEM

6. USES
 Sign language recognition
 Facial Expressions
 Face Recognition
 Electrical Field Sensing
 Vision Based Technologies
 Device Gesture Technologies

7. SIGN LANGUAGE RECOGNITION
Effective Sign language recognition would grant the deaf
expanded tools for communicating with both other people and
machine.
Certain types of gesture recognition software can transcribe
the symbols represented through sign language into text.

8. FACIAL EXPRESSIONS
 Facial gesture recognition creates
an effective non- contact interface
between users and their machines.
 The goal of facial gesture
recognition is to make machine
effectively understand human
emotions of the countless physical
difference between individual.
 Facial gesture recognition can be
used for purposes related to
personal security & law enforcement

9. FACE RECOGNITION
The Face Recognition Technology (FERET) program
database is a large database of facial images,
divided into development and ... Maximum likelihood
detection of faces and hands.
International Workshop on Automatic Face and Gesture
Recognition

10. ELECTRICAL FIELD SENSING
Proximity of a human body or body part can be measured by
sensing electric fields .
These measurements can be used to measure the distance
of a human hand or other body part from an object; this
facilitates a vast range of applications for a wide range of
industries.

11. VISION BASED TECHNOLOGIES
 There are two approaches to vision based
gesture recognition:
 Model based techniques:
They try to create a three dimensional model of the users hand
and use this for recognition.
 Image based methods:
Image-based techniques detect a gesture by capturing
pictures of a user’s motions during the course of a gesture.

12. DEVICE GESTURE TECHNOLOGIES
 Device-based techniques use a glove, stylus, or other
position tracker, whose movements send signals that the
system uses to identify the gesture.
 The glove is equipped with a variety of sensors to provide
information about hand position, orientation, and flex of
fingers.

13. INPUT DEVICES
 Wired gloves
 Depth-aware cameras
 Stereo cameras
 Controller-based gestures
 Single camera

14. WIRED GLOVES
 A wired glove (also called a "data glove“ is
an input device for human–computer
interaction worn like a glove.

15. DEPTH-AWARE CAMERAS
 Depth sensors’ ability to capture large amounts of 3D
information will completely change the way that digital
photography is done. Users would be able to seamlessly
remove objects, isolate single objects, and even remove
and replace the entire background of a picture.

16.  Navigation software like Google Maps would be more
accurate than ever by combining GPS data with the ability to
accurately sense the user’s immediate surroundings; they
could offer the ability to navigate inside of buildings. Devices
could even be developed that allow the visually impaired to
navigate the world around them, and the hearing impaired
could have sign language translated in real time.

17. STEREO CAMERAS
 A stereo camera is a type of
camera with two or more
lenses with a separate
image sensor or film frame
for each lens. This allows
the camera to simulate
human binocular vision,
and therefore gives it the
ability to capture three-
dimensional images, a
process known as stereo
photography.

18. CONTROLLER-BASED GESTURES
 These controllers act as an extension of the body so that
when gestures are performed, some of their motion can be
conveniently captured by software.

19. SINGLE CAMERA
 The single-camera setup, or single-camera
mode of production, also known as Portable
Single Camera, is a method of filmmaking
and video production.

20. ALGORITHMS
 3D model-based algorithms
 Appearance-based models

21. 3D MODEL-BASED ALGORITHMS
 The 3D model approach can use volumetric or skeletal
models, or even a combination of the two. Volumetric
approaches have been heavily used in computer
animation industry and for computer vision purposes.
The models are generally created of complicated 3D
surfaces, like NURBS or polygon meshes.
 The drawback of this method is that is very
computational intensive, and systems for live analysis
are still to be developed.

22. SKELETAL-BASED ALGORITHMS
 Instead of using intensive processing of the 3D models
and dealing with a lot of parameters, one can just use a
simplified version of joint angle parameters along with
segment lengths. This is known as a skeletal
representation of the body, where a virtual skeleton of
the person is computed and parts of the body are
mapped to certain segments.

23. APPEARANCE-BASED MODELS
 These models don’t use a spatial representation of the
body anymore, because they derive the parameters
directly from the images or videos using a template
database. Some are based on the deformable 2D
templates of the human parts of the body, particularly
hands. Deformable templates are sets of points on the
outline of an object, used as interpolation nodes for the
object’s outline approximation.

24. CHALLENGES
 There are many challenges associated with the accuracy
and usefulness of gesture recognition software. For image-
based gesture recognition there are limitations on the
equipment used and image noise. Images or video may not
be under consistent lighting, or in the same location. Items
in the background or distinct features of the users may
make recognition more difficult.

25. FLUTTER APP
 Flutter allows you to play & pause Spotify, iTunes,
Quicktime, and VLC (latest version) using gestures via
your built-in webcam.
- Play/Pause your music & videos using simple hand
gestures
- Works via your webcam -- no additional hardware
necessary
- Works best from 1-6 feet away from webcam
- Works even when Spotify & iTunes is playing music in
the background
Coming Soon
- Gestures for Skip & Like Songs
- Mute
- Volume Control

26. Since the official debut in late May on the Mac App Store,
Flutter has been quite popular worldwide. Within two
weeks of their debut in the App Store, Flutter became part of
the top 5 downloads in over 30 countries among all free
entertainment apps. Users in over 90 countries have
downloaded it. At one point the app was #2 in the United
States, along with being the #1 free entertainment app in 14
countries, as well as the overall #1 free app in Korea, Italy,
Sweden, and Taiwan. https://flutterapp.com

27. FUTURE
 Currently, gesture recognition is mostly commonly used
in gaming applications but the potential for future
applications is immense. What if we could use gesture
recognition to manage multimedia in our home
environment? Use a gesture to turn our TV or heating
system on and off? What if we could use gesture
recognition to teach, do remote surgeries or conduct
facial recognition in airport security lines?