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Gesture recognition is a topic in computer science and 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. Current focuses in the field include emotion recognition from face and hand gesture recognition. Users can use simple gestures to control or interact with devices without physically touching them. Many approaches have been made using cameras and computer vision algorithms to interpret sign language. However, the identification and recognition of posture, gait, proxemics, and human behaviors is also the subject of gesture recognition techniques. Gesture recognition can be seen as a way for computers to begin to understand human body language, thus building a richer bridge between machines and humans than primitive text user interfaces or even GUIs (graphical user interfaces), which still limit the majority of input to keyboard and mouse.
Contents
- Gesture types
- Input devices
- Algorithms
- 3D model based algorithms
- Skeletal based algorithms
- Appearance based models
- Challenges
- Gorilla arm
- References
Gesture recognition enables humans to communicate 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. This could make conventional input devices such as mouse, keyboards and even touch-screens redundant.
Gesture Recognition Features:
The major application areas of gesture recognition in the current scenario are:
Gesture recognition technology has been considered to be the highly successful technology as it saves time to unlock any device.A person doesn't need to remember the security codes like passwords for its device, with the help of gesture recognition the person can unlock its device without any work.Mainly hand gestures are applicable. Gesture recognition can be conducted with techniques from computer vision and image processing.
The literature includes ongoing work in the computer vision field on capturing gestures or more general human pose and movements by cameras connected to a computer.
Gesture recognition and pen computing: Pen computing reduces the hardware impact of a system and also increases the range of physical world objects usable for control beyond traditional digital objects like keyboards and mice. Such implementations could enable a new range of hardware that does not require monitors. This idea may lead to the creation of holographic display. The term gesture recognition has been used to refer more narrowly to non-text-input handwriting symbols, such as inking on a graphics tablet, multi-touch gestures, and mouse gesture recognition. This is computer interaction through the drawing of symbols with a pointing device cursor. (see Pen computing)
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.
Input devices
The ability to track a person's movements and determine what gestures they may be performing can be achieved through various tools. The kinetic user interfaces (KUIs) are an emerging type of user interfaces that allow users to interact with computing devices through the motion of objects and bodies. Examples of KUIs include tangible user interfaces and motion-aware games such as Wii and Microsoft's Kinect,and other interactive projects.
Although there is a large amount of research done in image/video based gesture recognition, there is some variation within the tools and environments used between implementations.
Another example of this is mouse gesture trackings, where the motion of the mouse is correlated to a symbol being drawn by a person's hand, as is the Wii Remote or the Myo armband or the mForce Wizard wristband, which can study changes in acceleration over time to represent gestures. Devices such as the LG Electronics Magic Wand, the Loop and the Scoop use Hillcrest Labs' Freespace technology, which uses MEMS accelerometers, gyroscopes and other sensors to translate gestures into cursor movement. The software also compensates for human tremor and inadvertent movement. AudioCubes are another example. The sensors of these smart light emitting cubes can be used to sense hands and fingers as well as other objects nearby, and can be used to process data. Most applications are in music and sound synthesis, but can be applied to other fields.
Algorithms
Depending on the type of the input data, the approach for interpreting a gesture could be done in different ways. However, most of the techniques rely on key pointers represented in a 3D coordinate system. Based on the relative motion of these, the gesture can be detected with a high accuracy, depending on the quality of the input and the algorithm’s approach.
In order to interpret movements of the body, one has to classify them according to common properties and the message the movements may express. For example, in sign language each gesture represents a word or phrase. The taxonomy that seems very appropriate for Human-Computer Interaction has been proposed by Quek in "Toward a Vision-Based Hand Gesture Interface". He presents several interactive gesture systems in order to capture the whole space of the gestures:
- Manipulative
- Semaphoric
- Conversational
Some literature differentiates 2 different approaches in gesture recognition: a 3D model based and an appearance-based. The foremost method makes use of 3D information of key elements of the body parts in order to obtain several important parameters, like palm position or joint angles. On the other hand, Appearance-based systems use images or videos for direct interpretation.
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 from complicated 3D surfaces, like NURBS or polygon meshes.
The drawback of this method is that is very computational intensive, and systems for real time analysis are still to be developed. For the moment, a more interesting approach would be to map simple primitive objects to the person’s most important body parts ( for example cylinders for the arms and neck, sphere for the head) and analyse the way these interact with each other. Furthermore, some abstract structures like super-quadrics and generalised cylinders may be even more suitable for approximating the body parts. The exciting thing about this approach is that the parameters for these objects are quite simple. In order to better model the relation between these, we make use of constraints and hierarchies between our objects.
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. The analysis here is done using the position and orientation of these segments and the relation between each one of them( for example the angle between the joints and the relative position or orientation)
Advantages of using skeletal models:
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. One of the simplest interpolation function is linear, which performs an average shape from point sets, point variability parameters and external deformators. These template-based models are mostly used for hand-tracking, but could also be of use for simple gesture classification.
A second approach in gesture detecting using appearance-based models uses image sequences as gesture templates. Parameters for this method are either the images themselves, or certain features derived from these. Most of the time, only one ( monoscopic) or two ( stereoscopic ) views are used.
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.
The variety of implementations for image-based gesture recognition may also cause issue for viability of the technology to general usage. For example, an algorithm calibrated for one camera may not work for a different camera. The amount of background noise also causes tracking and recognition difficulties, especially when occlusions (partial and full) occur. Furthermore, the distance from the camera, and the camera's resolution and quality, also cause variations in recognition accuracy.
In order to capture human gestures by visual sensors, robust computer vision methods are also required, for example for hand tracking and hand posture recognition or for capturing movements of the head, facial expressions or gaze direction.
"Gorilla arm"
"Gorilla arm" was a side-effect of vertically oriented touch-screen or light-pen use. In periods of prolonged use, users' arms began to feel fatigue and/or discomfort. This effect contributed to the decline of touch-screen input despite initial popularity in the 1980s.
In order to measure arm fatigue and the gorilla arm side effect, researchers developed a technique called Consumed Endurance.