关键词: 车脸, 经验矩形, 局部不变性特征, Adaboost

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