20-12-2012, 04:47 PM
FACE DETECTION USING IMAGE PROCESSING
INTRODUCTION
Face Detection has been one of the most studied topics in the computer vision literature. It is one of the fundamental techniques that enables natural human computer interaction (HCI).
Face Detection can be regarded as a specific case of object-class detection. Given an arbitrary image, the goal of face detection is to determine whether or not there are any faces in the image and, if present, return the image location and extent of each face. The difficulty associated with face detection can be attributed to many variations in scale, location, orientation (in-plane rotation), pose (out-of-plane rotation), facial expression, lighting condition, etc.
Many methods have been proposed to resolve each variation listed above. For example, the template-matching methods are used for face localization and detection by computing the correlation of an input image to a standard face pattern. The feature invariant approaches are used for feature detection of eyes, mouth, ears, nose, etc. The appearance-based methods are used for face detection with eigen face, neural network and information theoretical approach.
APPLICATIONS
Face Detection is used in biometrics often as a part of (or together with) a facial recognition system. It is also used in video surveillance, human computer interface and image database management. Some recent digital cameras use face detection for autofocus.
It acts as a ‘smart’ person recognizer for many applications without human intervention. A webcam can be integrated into a television and detect any face that walks by. The system then calculates the race, gender, and range of the face.
Face detection is also being researched in the area of energy conservation. Televisions and computers can save energy by reducing the brightness. People tend to watch TV while doing other tasks and not focussed 100% on the screen. The TV brightness stays at the same level unless it is lowered manually. The system can recognize the face direction of the TV user. When the user is not looking at the screen, the TV brightness is lowered. When the face returns to the screen, the brightness is increased.