CAJ | 학술논문

在表情识别中Gabor结合局部二值模式（LBP）的特征提取方法以及直方图统计降维虽然是较为局部化的方法，但LBP鲁棒性较差，识别精度不高，而且使用直方图统计来区分表情，其计算复杂度和特征维数依旧较高。中心对称局部二值模式（CS-LBP）与LBP相比具有较好的鲁棒性，但其对表情纹理细节的描述仍不够详细。因此提出基于Gabor结合改进的CS-LBP即二值叠加中心对称局部二值模式（二值叠加CS-LBP）的特征提取方法。用Gabor提取特征，同时用两种计算方式提取两个特征值并叠加，作为最终识别的特征；并通过离散余弦变换（DCT）降维，有效降低表情的特征维数。在JAFFE表情库中实验验证了该方法能有效提高识别精度。
재표정식별중Gabor결합국부이치모식（LBP）적특정제취방법이급직방도통계강유수연시교위국부화적방법，단LBP로봉성교차，식별정도불고，이차사용직방도통계래구분표정，기계산복잡도화특정유수의구교고。중심대칭국부이치모식（CS-LBP）여LBP상비구유교호적로봉성，단기대표정문리세절적묘술잉불구상세。인차제출기우Gabor결합개진적CS-LBP즉이치첩가중심대칭국부이치모식（이치첩가CS-LBP）적특정제취방법。용Gabor제취특정，동시용량충계산방식제취량개특정치병첩가，작위최종식별적특정；병통과리산여현변환（DCT）강유，유효강저표정적특정유수。재JAFFE표정고중실험험증료해방법능유효제고식별정도。
In facial expression recognition, the feature extraction methods such as Gabor combined with Local Binary Pat-tern(LBP)and histograms for dimension reduction are more localization methods, but the LBP is of poor robustness and low precision for recognition, and the computational complexity and the dimension of characteristics are still high for his-tograms. Compared with LBP, Center-Symmetric Local Binary Pattern(CS-LBP)is more robust, but its description for facial expression texture is insufficiently detailed. This paper proposes a new feature extraction method based on Gabor com-bined with improved CS-LBP. The improved CS-LBP is addition of two-valued center-symmetric local binary pattern. The method uses Gabor to extract feature, and the final recognition feature is the superposition of two different features calculated by two methods. The dimensions of the final recognition feature are effectively reduced by Discrete Cosine Transform(DCT). The proposed method is tested by using JAFFE facial expression database. The result shows that the method can effectively improve the recognition accuracy.