Computer vision is one of the foremost fields which have experienced an increasing number of applications in the recent years in various directional domains like biomedical imaging, surveillance systems, interactive systems like gesture, recognition, gaming, etc. Detection of human faces is one of the key elements in the applications of computer vision in the above-mentioned domains. Face detection is based on identifying and locating a human face in the image regardless of size, position, and condition.  Facial feature detection methods generally model two types of information. The first is local texture around a given feature, for example, the pixel values in a small region around an eye. The second is the
geometric configuration of a given set of facial features, e.g. eyes, nose, mouth, etc.

    This design is to detect faces in real-time with very high detection rate. It is essentially a feature-based approach, in which a classifier is trained for Haar-like rectangular features selected by AdaBoost algorithm and efficient representation method histogram equalization is used for varying illumination in the image. The face detection system generates an integral image window to perform a Haar-feature classification during one clock cycle. And then it performs classification operations in parallel using Haar classifiers to detect a face in the image sequence. The classifiers at the beginning of the cascade are simpler and consist of smaller numbers of features. However, as one proceeds in the cascade, the classifiers become more complex. A region is reported as detection only if it passes all the classifier stages in the cascade. If it is rejected at any stage, it is discarded and not processed further. If all stages are passed the face of a candidate is concluded to be recognized the face.

Reference Paper: FPGA Based Real-Time Face Detection using Adaboost and Histogram Equalization
Author’s Name: K.Padmaja and T.N.Prabakar
Source: IEEE
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