INTRODUCTION
Face detection in image sequence has been an active research area in the computer vision field in recent years due to its potential applications such as monitoring and surveillance, human computer interfaces, smart rooms, intelligent robots, and biomedical image analysis. Face detection is based on identifying and locating a human face in images regardless of size, position, and condition. Numerous approaches have been proposed for face detection in images. Simple features such as color, motion, and texture are used for the face detection in early researches. However, these methods break down easily because of the complexity of the real world. Face detection proposed by Viola and Jones is most popular among the face detection approaches based on statistic methods. This face detection is a variant of the AdaBoost algorithm which achieves rapid and robust face detection. They proposed a face detection framework based on the AdaBoost learning algorithm using Haar features. However, the face detection requires considerable computation power because many Haar feature classifiers check all pixels in the images. Although real-time face detection is possible using high performance computers, the resources of the system tend to be monopolized by face detection. Therefore, this constitutes a bottleneck to the application of face detection in real time.
FACE DETECTION ALGORITHM
The face detection algorithm proposed by Viola and Jones is used as the basis of our design. The face detection algorithm looks for specific Haar features of a human face. When one of these features is found, the algorithm allows the face candidate to pass to the next stage of detection. A face candidate is a rectangular section of the original image called a subwindow. Generally these sub-windows have a fixed size (typically 24×24 pixels). This sub-window is often scaled in order to obtain a variety of different size faces. The algorithm scans the entire image with this window and denotes each respective section a face candidate. The algorithm uses an integral image in order to process Haar features of a face candidate in constant time. It uses a cascade of stages which is used to eliminate non-face candidates quickly. Each stage consists of many different Haar features. Each feature is classified by a Haar feature classifier. The Haar feature classifiers generate an output which can then be provided to the stage comparator. The stage comparator sums the outputs of the Haar feature classifiers and compares this value with a stage threshold to determine if the stage should be passed. If all stages are passed the face candidate is concluded to be a face.
VIDEO DEMO
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