A method on Face Recognition of Contaminated Small Sample

Abstract

In recent years, face recognition technology has been rapidly promoted and used because of its advantages of convenience, non-contact and good recognition performance, and achieved good results. But for the polluted situation, such as expression change, illumination influence, posture change, occlusion, etc., because the collected image lost the original intrinsic characteristics, seriously affect the effect of face recognition. In recent years, the SRC method and its variants have good robustness to shaded and contaminated samples. However, when certain training samples and test samples are contaminated, the performance will degrade. The method based on low rank matrix recovery (LRMR) can effectively deal with the simultaneous contamination of training samples and test samples. However, they either ignore relationships between similar samples or fail to learn a compact dictionary from contaminated training data. To solve these problems, a locally constrained low-rank representation and dictionary learning algorithm (LCLRRDL) is developed and applied to robust face recognition. In this paper, the theoretical and experimental research of locally constrained low-rank representation and dictionary learning algorithm (LCLRRDL) is carried out. A low-rank representation is introduced to deal with possible contamination of training and test data, and local constraints are introduced to identify the inherent manifold structure of training data. Under local constraints, similar samples often have similar representations, and the learned representations can be directly used for classification. At the same time, this method can learn a compact dictionary with better refactoring and recognition ability. The superiority of LCLRRDL algorithm is verified by comparing it with support vector machine (SVM) and Sparse representation (SRC) for contaminated small sample face recognition. By comparing the best recognition effect and classification time of the three algorithms, it is found that the three algorithms are robust to contaminated face recognition for two different face databases. LCLRRDL algorithm has the highest recognition rate, but its disadvantage is that the classification recognition time is long. Therefore, when the actual application does not require the recognition speed, the recognition rate of LCLRRDL algorithm is more suitable for high-precision face recognition.