About Happytime Face Detection
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...
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.
Previous Versions
Here you can find the changelog of Happytime Face Detection since it was posted on our website on 2014-04-20 00:19:02.
The latest version is 2.0 and it was updated on 2024-04-22 19:56:50. See below the changes in each version.
Happytime Face Detection version 2.0
Updated At: 2013-10-20
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, written in C. can easily be ported.
Disclaimer
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