AdaBoost Face Detection
نویسنده
چکیده
Viola and Jones [1] introduced a new and effective face detection algorithm based on simple features trained by the AdaBoost Algorithm, Integral Images and Cascaded Feature sets. This paper attempts to replicate their results. The Feret Face data set is used as the training set. The AdaBoost Algorithm, simple feature set and Integral Images are briefly explained and implemented in our Matlab based program. A series of ten best features were identified out of a set of close to fifty thousand. These best features were used to produce probability of error plots. Finally our face detection Algorithm is implemented on a series or random Images taken from the internet. More than just ten best features are needed to have a face detector comparable to the two hundred best features of Viola and Jones [1] but the face detector still performs well and anyone can use our program included in the Appendix to implement this effective face detection algorithm and train as many best features as suited for their application..
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Efficient face detection method with eye region judgment
Real-time face detection technology can be applied in many industrial or commercial products. Many face detection applications use the traditional Adaboost face detection system which is proposed by Viola and Jones in 2004. Viola and Jones used the Adaboost training algorithm and the Haar-like features in their proposed traditional Adaboost face detection system, which has a high detection rate...
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