https://www.cin.ufpe.br/~rps/Artigos/Face%20Detection%20-%20%20A%20Survey.pdf

Face Detection

Architecture for implementing a perceptual user interface. The architecture comprises alternative modalities for controlling computer application programs and manipulating on-screen objects through hand gestures or a combination of hand gestures and verbal commands. The perceptual user interface system includes a tracking component that detects object characteristics of at least one of a plurality of objects within a scene, and tracks the respective object. Detection of object characteristics is based at least in part upon image comparison of a plurality of images relative to a course mapping of the images. A seeding component iteratively seeds the tracking component with object hypotheses based upon the presence of the object characteristics and the image comparison. A filtering component selectively removes the tracked object from the object hypotheses and/or at least one object hypothesis from the set of object hypotheses based upon predetermined removal criteria.

Architecture for controlling a computer using hand gestures

A system and method for recognizing gestures. The method comprises obtaining image data and determining a hand pose estimation. A frontal view of a hand is then produced. The hand is then isolated the background. The resulting image is then classified as a type of gesture. In one embodiment, determining a hand pose estimation comprises performing background subtraction and computing a hand pose estimation based on an arm orientation determination. In another embodiment, a frontal view of a hand is then produced by performing perspective unwarping and scaling. The system that implements the method may be a personal computer with a stereo camera coupled thereto.

System and method for gesture recognition in three dimensions using stereo imaging and color vision

Techniques for facilitating interaction with an application in a motion capture system allow a person to easily begin interacting without manual setup. A depth camera system tracks a person in physical space and evaluates the person's intent to engage with the application. Factors such as location, stance, movement and voice data can be evaluated. Absolute location in a field of view of the depth camera, and location relative to another person, can be evaluated. Stance can include facing a depth camera, indicating a willingness to interact. Movements can include moving toward or away from a central area in the physical space, walking through the field of view, and movements which occur while standing generally in one location, such as moving one's arms around, gesturing, or shifting weight from one foot to another. Voice data can include volume as well as words which are detected by speech recognition.

Recognizing user intent in motion capture system

In a motion capture system having a depth camera, a physical interaction zone of a user is defined based on a size of the user and other factors The zone is a volume in which the user performs hand gestures to provide inputs to an application The shape and location of the zone can be customized for the user The zone is anchored to the user so that the gestures can be performed from any location in the field of view Also, the zone is kept between the user and the depth camera even as the user rotates his or her body so that the user is not facing the camera A display provides feedback based on a mapping from a coordinate system of the zone to a coordinate system of the display The user can move a cursor on the display or control an avatar

Physical interaction zone for gesture-based user interfaces

We propose a multi-camera system that can track multiple human faces and hands as well as focus on face and hand gestures for recognition. Our current system consists of four cameras. Two fixed cameras are used as a stereo system to estimate face and hand positions. The stereo camera detects faces and hands by a standard skin color method we propose. The distances of the targets are then estimated. Next to track multiple targets, we estimate the positions and sizes of targets between consecutive frames. The other two cameras perform tracking of such targets as faces and hands. If a target is not the appropriate size for recognition, the tracking cameras acquire its zoomed image. Since our system has two tracking cameras, it can track two targets at the same time. To recognize faces and hand gestures, we propose four directional features by using linear discriminant analysis. Using our system, we experimented on human position estimation, multiple face tracking, and face and hand gesture recognition. These experiments showed that our system could estimate human position with the stereo camera and track multiple targets by using target positions and sizes even if the persons overlapped with each other. In addition, our system could recognize faces and hand gestures by using the four directional features.

Focus of attention for face and hand gesture recognition using multiple cameras

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Focus of attention for face and hand gesture recognition using multiple cameras