中圖分類號： TP391.4 文獻(xiàn)標(biāo)識碼： A DOI：10.16157/j.issn.0258-7998.200545 中文引用格式： 譚兆海，李育林，張璇，等. 塊LBP-TOP稀疏表示表情與車輛檢測技術(shù)研究[J].電子技術(shù)應(yīng)用，2020，46(12)：53-56. 英文引用格式： Tan Zhaohai，Li Yulin，Zhang Xuan，et al. Sparse representation for micro-expression and vehicle status recognition based on blocked LBP-TOP[J]. Application of Electronic Technique，2020，46(12)：53-56.
Sparse representation for micro-expression and vehicle status recognition based on blocked LBP-TOP
Tan Zhaohai1，Li Yulin1，Zhang Xuan2，Sun Ning3，Liu Wenwen3，Yang Su3
1.China Railway Lanzhou Bureau Groups Co.，Ltd.，Lanzhou 730000，China； 2.School of Information and Engineering，Yanshan University，Qinhuangdao 066004，China； 3.Suzhou NewVision Science and Technology Co.，Ltd.，Suzhou 215000，China
Abstract： In this paper, a micro-expression and vehicle status recognition method based on blocked local binary pattern from three orthogonal planes(LBP-TOP) features and weighted sparse representation as the classifier is proposed. First of all, the effective block is selected from the blocked image. Then, the features, which are extracted from LBP-TOP feature descriptor, are used as a dictionary. Then the combined weighted sparse representation(WSRC) and the dual augmented lagrangian multiplier(DALM) algorithm performs sparse representation classification. Finally, the images are divided to different sizes blocks, then the effective block is chosen from these blocks, and the features are merged as the input to the classifier. The experiments are carried out on the CASME Ⅱ，SAMM and vehicle databases using leave-one-subject-out cross validation(LOSOCV). When classifying the micro-expressions into five categories, the classification accuracy can reach separately 77.30% and 58.82%, and the experiment on the database of vehicle state detection reaches 84.60% detection rate. Experimental results show the effectiveness of the proposed algorithm.
Key words : micro-expression；vehicle status detection；block-based LBP-TOP；sparse representation
