Research on Facial Feature Point Detection Technology Based on AdaBoost and AAM
This paper reports the current status of facial feature point detection and analyzes the classification performance of the AdaBoost algorithm and the modeling characteristics of the AAM model.It researches the facial feature point detection,and improves the accuracy and robustness by training multiple weak classifiers and combining them.It uses the results identified by the AdaBoost strong classifier as inputs for training the AAM model,extracts candidate regions for facial feature points,reduces the reconstruction frequency of the AAM model and further lowers the computational complexity,particularly in cases where there are significant variations in facial pose and expression,thereby improving the matching accuracy.Additionally,the AAM model can provide a more precise localization of facial feature points for AdaBoost,thus enhancing the overall performance of facial feature point detection.
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