Abstract: Focused on those vehicles with unrecognized license plate, this paper proposes a new approach for vehicle face localization and vehicle face recognition. The approach is divided into two steps: Firstly, adaptive boosting (Adaboost) algorithm improved by experience rectangle method proposed in this paper is used for vehicle face localization. Secondly, scale-invariant feature transform (SIFT) and speeded up robust features (SURF) local invariant features are extracted from the detected vehicle face, vehicle face recognition algorithm is improved by adding location constraints to matched points pair and combining SIFT and SURF. Then the vehicle type is recognized by comparing it with the same features stored in the standard vehicle type database. Experiments show that the algorithm can locate vehicle face quickly and effectively, the correct recognition rate reaches 83.6%. Images captured on the highway bayonet are mostly from the front view, so the vehicle body side information to analyze vehicle type cannot be obtained. To those vehicles with unrecognized license plate, the approach by vehicle face localization, feature extraction and vehicle face recognition by comparison with each vehicle face stored in the standard vehicle type database provides a new solution for vehicle type recognition.

Key words: vehicle face, experience rectangle, local invariant features, Adaboost

摘要： 针对车牌无法识别的车辆，研究了一种车脸定位及识别方法。该方法分为两个阶段：首先，使用Adaboost算法进行车脸定位，并利用经验矩形方法进行定位改进；其次，在定位出来的车脸区域提取SIFT（scale-invariant feature transform）和SURF（speeded up robust feature）局部不变性特征，利用这两种不变性特征的叠加及位置约束改进匹配算法，与标准车型数据库中的车脸特征进行匹配，根据匹配结果进行车脸识别，从而得到车辆类型。实验结果表明，该方法的正确识别率达到83.6%。交通卡口抓拍到的车辆照片基本是正前照，无法获取车身侧面信息分析其车型。针对车牌无法识别的车辆，通过车脸定位、特征提取，并与标准车型库中车脸进行对比，进而识别车脸，该识别车脸的方法为识别车型提供了一种新途径。

关键词: 车脸, 经验矩形, 局部不变性特征, Adaboost