Abstract: Facing with multi-category samples with limited number of annotations,in order to improve vehicle face recognition accuracy,a vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization(NMF)was proposed. Firstly,the shape feature of local region of vehicle face image was extracted by Histogram of Oriented Gradients (HOG)operator,which was used as the original feature of vehicle face image. Then,the NMF model with multiple weights, orthogonality and sparse constraints was proposed,based on which,the feature bases describing the vehicle face image key regions were acquired,and the feature dimension reduction was achieved. Finally,the discrete cosine distance was used to calculate the similarity between features,and it was able to be concluded that whether the vehicle face images were matched or not. Experimental results show that the proposed recognition algorithm can obtain good recognition effect with accuracy of 97. 68% on the established vehicle face image dataset,at the same time,the proposed algorithm can meet the real-time requirement.

Key words: vehicle face recognition, Nonnegative Matrix Factorization (NMF), gradient descent method, feature dimension reduction, discrete cosine distance

摘要： 面对多类别且标注数量有限的样本，为进一步提高车脸图像的识别准确性，提出一种基于改进非负矩阵分解（NMF）的车脸识别算法。首先，采用方向梯度直方图（HOG）算子提取车脸图像局部区域形状特征，并将其作为车脸图像的初始特征；而后，提出具有多权重、正交性、稀疏性约束的NMF模型，并基于该模型获得了描述车脸图像中关键区域的特征基，实现了特征的降维；最后，利用离散余弦距离计算特征间的相似性，进而对车脸图像是否匹配作出判断。实验结果表明，对于建立的车脸图像数据集，提出的识别算法能够取得较好的识别效果，准确率可达到97.56%，且满足实时性要求。

关键词: 车脸识别, 非负矩阵分解, 梯度下降法, 特征降维, 离散余弦距离