Happytime Face Detection 2.0

Happytime face detection algorithm can accurately detect human faces, with fewer false detection, high accuracy. It can be used for still pictures and video to detect faces. The algorithm code don't rely oepncv library (The demo application only use opencv read image file), written in C, can easily be ported.

Key features:
Low false detection
High accuracy
Writted in C
Can be portable

algorithm principle:
MBLBP-based lookup table type weak classifiers Real AdaBoost face detection algorithm.
LBP (Local Binary Pattern) characterized by the Ojala made in 1994, and applied to the problem of texture classification. MBLBP feature is an extension of the use of image blocks instead of the original LBP features a single pixel as the basic unit, which can reduce the image noise calculation LBP features, if adopt integral image technique, it is possible to be obtained MBLBP features in constant computation time.

Algorithm evaluation:
MBLBP lookup table type weak classifiers Real AdaBoost face detection algorithm and other published methods were compared, the compare results of face detection algorithm from FDDB official results, The official description of the specific method reference FDDB. The results shown in Figure, it can be seen from the figure, MBLBP lookup table type weak classifiers Real AdaBoost face detection algorithm (MBLBP (LUT)) exceed other methods

Requirements:	No special requirements
Release Date:	October 20, 2013
Platforms:	Windows XP, Win7 x32, Win7 x64, WinServer, WinVista
Keyword:	Face Detection,  Mblbp,  Real Adaboost
Version 2.0 Changes:	Happytime face detection algorithm can accurately detect human faces, with fewer false detection, high accuracy. It can be used for still pictures and video to detect faces. The algorithm code don't rely oepncv library (The demo application only use opencv read image file), written in C.
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