南京理工大学学报（自然科学版）2017，Vol.41Issue(6)：714-719,6.DOI:10.14177/j.cnki.32-1397n.2017.41.06.008
基于改进的快速鲁棒特征算法的人脸检测研究
Face detection based on improved speeded up robust features algorithm
洪杨 1于凤芹1
作者信息
- 1. 江南大学 物联网工程学院,江苏 无锡214122
- 折叠
摘要
Abstract
An improved speeded up robust features( SURF) algorithm is used in face detection aming at the shortcomings of many redundant information and low computing speed of the SURF algorithm. The image entropy of neighborhood of each feature point is calculated, and the feature points with high image entropy are selected by non-maximum suppression to decrease description area and redundant information. A fan window is used to traverse the neighborhood of each feature point,and Haar wavelet response in the fan window is calculated to form the descriptor of each feature point, and the computing speed is faster. Each descriptor is mapped into a high dimensional space by Fisher vector kernel for face detection. The simulation results in face detection data set and benchmark ( FDDB) show that,compared with that of SURF algorithm,the detection rate of the improved SURF algorithm increases by 7 . 9％, the feature calculation time decreases by 53 . 1％, and the feature points decreases by 59 . 7％.关键词
快速鲁棒特征/人脸检测/图像熵/非极大值抑制/哈尔小波响应/费舍尔矢量核Key words
speeded up robust features/face detection/image entropy/non-maximum suppression/Haar wavelet response/Fisher vector kernel分类
信息技术与安全科学引用本文复制引用
洪杨,于凤芹..基于改进的快速鲁棒特征算法的人脸检测研究[J].南京理工大学学报（自然科学版）,2017,41(6):714-719,6.
