Face recognition is specifically recognized what running algorithms are

1, face detection in practice is mainly used for face recognition preprocessing, that is, in the image to accurately calibrate the location and size of the face. Face images contain a wealth of pattern features, such as histogram features, color features, template features, structural features and Haar features. Face detection is to pick out this useful information and use these features to achieve face detection.

2, the mainstream face detection methods based on the above features using Adaboost learning algorithm, Adaboost algorithm is a method used to classify, it puts some of the weaker classification methods together, combining the new very strong classification methods.

3, face detection process using the Adaboost algorithm to pick out some of the most representative of the face of the rectangular features (weak classifiers), according to the weighted voting of the weak classifiers constructed as a strong classifiers, and then training a number of strong classifiers in series composed of a cascade structure of the cascade of classifiers, effectively improve the speed of classification of the detection of classifiers.