摘要
为了提高在光照过度、不足或不均等复杂光照条件下的人脸识别率,提出一种复杂光照条件的人脸图像细节强化算法。首先采用对数和非线性变换对人脸图像动态范围进行压缩;然后利用反锐化掩模滤波算法消除图像模糊,增强人脸图像细节信息;最后采用Adaboost算法建立人脸分类器,并采用Yale B人脸图像数据进行仿真测试。仿真结果表明,该算法解决了复杂光照条件对人脸图像的不利影响,并进一步提高了人脸识别率。
In order to improve the rate of face recognition in excessive,inadequate,or non-uniform complex illumination conditions,a details strengthen al- gorithm for face image in the complex illunfination conditions is proposed in this paper. Firstly ,logarithmic and nonlinear transform is used to compress the dynamic range of face image. Then the unsharp mask filtering algorithm is used to remove the fuzzy image message to enhance the details of face image, Final- ly ,adatx）ost algorithm is used to build a classifier for face recognition,and the simulation test is carried out on Yale B face database. The simulation results show that the proposed algorithm has solved problem for face recognition in complex illumination conditions and improved the face recognition rate.
出处
《电视技术》
北大核心
2014年第3期12-15,26,共5页
Video Engineering
