基于ASM的改进人脸特征点标定算法

Liming Dai, Miao Liu, Yuanyuan Chen
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引用次数: 3

摘要

传统ASM的匹配结果受模型初始位置的影响较大。位置不当会导致算法失效。为了提高特征检测的精度,提出了一种快速粗糙定位模型(QRPM)算法,该算法利用图像和模型灰度信息计算模型与检测区域之间的相似系数。采用粗勘探和细勘探两种方法提取粗区,并在粗区设置初始模型。实验结果表明,改进后的算法可以有效地提高人脸特征点的标定精度,避免ASM结果陷入局部最小值。
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Improved Facial Feature Points Calibration Algorithm Based on ASM
The matching results of traditional ASM are greatly affected by the model initial position. An improper position will lead algorithm to fail. To enhance the accuracy of feature detection, a Quick Rough Positioning Model(QRPM) algorithm is proposed, which makes use of the image and model gray information to calculate the similar coefficient between the model and the region detected. Coarse and fine explorations are adopted to extract the rough region, where the initial model will be set. Experimental results show that the improved algorithm can effectively enhance the accuracy in facial feature points' calibration, and avoid the ASM results falling into the local minimum.
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