摘要
人脸识别技术需要对脸部特征进行定位,从而有助于确保图像一致和建立人脸模型。提出了一种新的脸部特征定位方法,通过Gabor滤波器处理得到人脸图像的强度响应,其中,脸部特征表现为强响应,而其他部分表现为弱响应,如面颊和额头。通过保留强响应以及过滤弱响应,可以获得属于脸部特征的所有像素点。采用了聚类算法——k均值算法将不同的像素点分配到不同的簇里面,每一个簇都代表一个脸部特征。通过在ORL人脸数据库上的测试表明:此方法能精确、快速地定位诸如眼睛、鼻子、嘴等脸部特征。此外,此方法能够在有浓密胡须的对象上成功定位脸部特征,表现出较高的鲁棒性。
The localization on facial features is needed for face recognition since it helps keeping accordance between face images and building face model.In this paper,a novel method for locating facial features was presented which included two steps： filtering and clustering.Face images were firstly processed by Gabor filter into magnitude responses.In the responses,facial features demonstrated relatively high magnitude responses than other facial parts,such as cheek and forehead.By reserving high magnitude responses and removing low magnitude responses,the pixel points belonging to facial features were collected.The method adopted a clustering approach—k-means for separating pixel points into different clusters.Each cluster represented a facial feature.By testing on the ORL face database,the method shows its accuracy and speed on locating facial features,such as eyes,nose and mouth.It also exhibits high robustness in locating features on faces which have thick beard or mustache.
出处
《红外与激光工程》
EI
CSCD
北大核心
2011年第3期576-580,共5页
Infrared and Laser Engineering
基金
精密测试及仪器国家重点实验室开放基金资助项目
