摘要
针对人脸在非均匀光照下识别率的降低,提出了拉普拉斯滤波和离散余弦变换(DCT)融合梯度方向直方图(HOG)人脸识别算法。首先通过拉普拉斯滤波对人脸图像进行处理,突出其纹理特征;其次进行离散余弦变换(DCT),有效滤除高频分量;然后利用离散余弦逆变换(IDCT)重建人脸图像,降低其维数;最后通过梯度方向直方图(HOG)算子提取人脸图像固有特征,并利用最近邻方法进行分类识别。实验结果表明,该算法在不同特征维数下的Yale B人脸数据库中识别率高达95%以及课题组自建的维吾尔族人脸数据库中其识别率高达98.5%,优于其他传统算法,具有很强的鲁棒性和实时性。
For the reduction of human face recognition rate under non-uniform illumination,Laplace filter and discrete cosine transform (DCT) gradient direction histogram (HOG) face recognition algorithm is proposed. First by Laplace filtering of face image processing,its texture feature is highlighted; Secondly in discrete cosine transform (DCT),high frequency components are effectively filtered out; Then using discrete cosine inverse transformation (IDCT) reconstruction of face image reduces its dimension; Finally through the gradient direction histogram (HOG) operator the inherent characteristics of facial image recognition are extracted and the nearest neighbor method is used. The experimental results show that the proposed algorithm under different characteristic dimension of human face recognition rate as high as 95% in the database and Yale B group of uighurs face database construction in its recognition rate is as high as 98.5%,is better than other traditional algorithm. This algorithm has strong robustness and real time.
引文
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