基于子模式的Gabor特征融合的单样本人脸识别

A Sub-Pattern Gabor Features Fusion Method for Single Sample Face Recognition

针对传统人脸识别方法在单训练样本条件下效果不佳的缺点,提出基于子模式的Gabor特征融合方法并用于单样本人脸识别.首先采用Gabor变换抽取人脸局部信息,为有效利用面部器官的空间位置信息,将Gabor人脸图像分块构成子模式,采用最小距离分类器对各子模式分类.最后对各子模式分类结果做决策级融合得出分类结果.根据子模式构成原则和决策级融合策略不同,提出两种子模式Gabor特征融合方法.利用ORL人脸库和CAS-PEAL-R1人脸库进行实验和比较分析,实验结果表明文中方法有效提高单样本人脸识别的正确率,改善单样本人脸识别系统的性能.

To overcome the limitations of traditional face recognition methods for single sample face recognition, a sub-pattern Gabor features fusion method for single sample face recognition is proposed. Firstly, facial local features are extracted by Gabor wavelet transformation. Then, the Gabor face images are blocked to take full advantage of the spatial location information of facial organs, and the minimum distance classifiers are used for each sub-pattern. Finally, the recognition result is achieved by the fusion of the sub-pattern classifiers＇ results at the decision level. According to the difference of sub-pattern construction and fusion method, two kinds of sub-pattern Gabor features integration programs are proposed. The experimental results and comparative analysis on ORL face database and CAS-PEAL-R1 face database show that the proposed method achieves better classification rate and improves the performance of single sample face recognition system.