KNLDA-based RBF neural network face recognition method
A network human and face recognition technology, applied in the field of computer vision and pattern recognition, can solve the problems that face recognition technology is not satisfactory and cannot be satisfied, and achieves the effect of solving the problem of small samples, strong classification ability and fast convergence speed.
Inactive Publication Date: 2017-05-17
重庆信科设计有限公司
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Problems solved by technology
In daily life, face recognition technology is not perfect enough, so long-term in-depth research has been carried out. In recent years, face recognition technology has made great progress, but it still cannot meet satisfactory requirements.
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[0031] The idea of the present invention is to solve the problem that the recognition rate is greatly reduced and the robustness is weakened under the interference of a series of natural factors such as illumination, posture and occluders in the existing face recognition method, and a kernel zero-space linear recognition method is proposed. The face recognition method of the RBF neural network of discriminant analysis (KNLDA) maps the input space to the high-dimensional feature space by introducing a kernel function, and the linear inseparable mode of the low-dimensional space can be linearly mapped to the high-dimensional feature space through nonlinear mapping. Separable, studies have shown that face recognition systems can be improved using RBF neural network classification as compared to classification based on the Euclidean distance metric. Improve the recognition rate of the face recognition method under large changes in illumination and posture, and partial occlusion, ...
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The invention relates to the technical field of face recognition. The face recognition method includes steps of combining advantages of null space linear distinguishing analysis (NLDA) and kernel function. The null space linear distinguishing analysis (NLDA) extracts identifying characteristics of a sample from a null space of a dispersion matrix of total classification of a training sample, thereby overcoming problems of small samples and improving the recognition rate; however, the NLDA is still an extracting method of the linear characteristics, and non-linear characteristics of the sample cannot be effectively extracted. Through the non-linear mapping, the method maps the input space sample to the high-dimensional characteristics space; the linear characteristic extracting algorithm to a high-dimensional characteristics space, thereby effectively extracting the non-linear characteristics of the sample. We use the RBP neutral network to identify the face image acquired through the characteristics extraction. The study indicates the recognition efficiency can be improved by comparing with a traditional classification method based on Europe type distance and other traditional measurements. The technical scheme provided by the invention can be well applied to the actual life, the recognition rate is higher, and the robustness is better.
Description
technical field [0001] The invention relates to a biological recognition method, in particular to a face recognition method based on an RBF neural network of Kernel Null Space Linear Discrimination Analysis (KNLDA), belonging to the fields of computer vision and pattern recognition. Background technique [0002] Face recognition is a key research topic in pattern recognition today. In the past 20 years, the research on face recognition technology has become a research hotspot, which is simple, direct and easy to be accepted by users. At present, face recognition technology has been widely used in the fields of document verification, intelligent monitoring, criminal investigation and case solving. However, due to the non-rigidity of face recognition, changes in illumination, posture changes, age changes, occlusion and other issues, these have an impact on face recognition algorithms. , to varying degrees. [0003] Due to the relatively high dimensionality of the face image,...
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Login to View More IPC IPC(8): G06K9/00G06N3/02
CPCG06N3/02G06V40/172G06V40/168
Inventor 文凯何强袁泉罗瑶
Owner 重庆信科设计有限公司