The strong classifier trained by mixed feature pool: Other embodiments of the invention, however, may exist in other circuits, logic units, or devices within the system of FIG. The template information is stored in a file known as a haar-cascade, usually formatted as an XML file [ 14 ]. Some known face detection algorithms implement the face detection task as a binary pattern classification task. A strong classifier built from U , with a trained threshold T , its prediction function H on sample x i is: Support Center Support Center.
Facial feature detection using Haar classifiers
For example, consider the image below. In establishing the image dataset, we employed twenty participants comprising fourteen males and six females, and obtained: Fifth, comparing the experiment group 5 with the control group, we can see face detection performance for thermal images drops for all the cascade classifiers trained by different feature pools even more than that of the experiment group 4. Following on, only a few windows remained for locating the exact number of faces via 12 stages with features. Minimum hit rate and maximum false alarm were set as 0. A strong classifier consists of many voters, with each voter including a weak classifier and its weight.
Real-Time Face Detection and Recognition in Complex Background
It was motivated primarily by the problem of face detection. Detecting faces in images: During detection, candidates of various sizes were obtained when the detector with multi-scale windows was moved over the original image. Groups of colocated detections that meet the threshold are merged to produce one bounding box around the target object. The size of the final bounding box is an average of the sizes of the bounding boxes for the individual detections and lies between MinSize and MaxSize.