Happytime face detection can accurately detect human faces, with fewer false detection, high accuracy. It can be used for still pictures and video to detect faces. It can simultaneously detect multiple faces, can detect different color face, can detect faces in a complex background. The algorithm code don't rely oepncv library (The application only use opencv read image file), written in C, can easily be ported. Key features: Low false detection, high accuracy Can simultaneously detect multiple faces Can detect different color face Can detect faces in a complex background Written in C, can easily be ported Algorithm principle: Based on MB-LBP(multi block local binary pattern) features lookup table type weak classifiers Real AdaBoost face detection algorithm. LBP (Local Binary Pattern) features proposed by the Ojala in 1994, and applied to the texture classification problem. MB-LBP feature is an extension of LBP, uses image blocks instead of the original LBP features which a single pixel as the basic unit. MB-LBP can reduce the image noise when calculate LBP features, if adopt integral image technique, it is possible to be obtained MBLBP features in constant computation time. AdaBoost is a boosting learning methods, AdaBoost training process using the threshold as a feature of weak classifiers output, this weak classifiers has limited ability to divide sample space. Based on Real AdaBoost algorithm, Wu proposed a lookup table type weak classifiers continuous AdaBoost face detection algorithm, to get a good face detection results. Algorithm evaluation: MB-LBP lookup table type weak classifiers Real AdaBoost face detection algorithm and other published methods were compared, the results shown in figure, it can be seen from the figure, MB-LBP lookup table type weak classifiers Real AdaBoost face detection algorithm exceed other methods.