Revision history [back]

Basically you have

1. First paper described the original approach by Viola and Jones on boosted cascades of weak classifiers to get a strong classifier. However that paper only uses a small set of basic upright features.
2. Then the second paper by Rainer describes an extended set of features, by rotating the features over 45 degrees and enabling more features from a given window to be used for classification. This resulted in a know accuracy improvement of trained models for several object classes, however not for all possible classes.
3. Is the paper that describes how the LBP features are implemented in OpenCV.

Aside from that users of the OpenCV community took the liberty of implementing a cascade classifier interface, also known as boosting for the LBP and HOG features. There is no paper exactly describing the approachm but the Histogram of Oriented Gradients is best described in the Dallal and Triggs paper where they combine it with SVM classification instead of boosting.

Basically you have

1. First paper described the original approach by Viola and Jones on boosted cascades of weak classifiers to get a strong classifier. However that paper only uses a small set of basic upright features.
2. Then the second paper by Rainer describes an extended set of features, by rotating the features over 45 degrees and enabling more features from a given window to be used for classification. This resulted in a know accuracy improvement of trained models for several object classes, however not for all possible classes.
3. Is the paper that describes how the LBP features are implemented in OpenCV.

Aside from that users of the OpenCV community took the liberty of implementing a cascade classifier interface, also known as boosting for the LBP and HOG features. There is no paper exactly describing the approachm but the Histogram of Oriented Gradients is best described in the Dallal and Triggs paper where they combine it with SVM classification instead of boosting.