Locally Linear Discriminate Embedding for Face Recognition
نویسندگان
چکیده
A novel method based on the local nonlinear mapping is presented in this research. The method is called Locally Linear Discriminate Embedding LLDE . LLDE preserves a local linear structure of a high-dimensional space and obtains a compact data representation as accurately as possible in embedding space low dimensional before recognition. For computational simplicity and fast processing, Radial Basis Function RBF classifier is integrated with the LLDE. RBF classifier is carried out onto low-dimensional embedding with reference to the variance of the data. To validate the proposed method, CMU-PIE database has been used and experiments conducted in this research revealed the efficiency of the proposed methods in face recognition, as compared to the linear and non-linear approaches.
منابع مشابه
Unified Locally Linear Embedding and Linear Discriminant Analysis Algorithm (ULLELDA) for Face Recognition
Manifold learning approaches such as locally linear embedding algorithm (LLE) and isometric mapping (Isomap) algorithm are aimed to discover the intrinsical low dimensional variables from high-dimensional nonlinear data. While, in order to achieve effective recognition tasks based on manifold learning, many problems remain to be solved. In this paper, we propose unified algorithm based on LLE a...
متن کاملComparing the Performance of Principal Component Analysis and RBF Network for Face Recognition using Locally Linear Embedding
Among the many methods proposed in the literature for face recognition, those relying on face manifold have been explored with great interest in the last few years. In those methods the face images are initially subjected to dimensional reduction and then applied to a classifier. In this paper we have proposed and developed two novel approaches for face recognition to address the challenging ta...
متن کاملDouble Linear Discriminate Analysis for Face Recognition
In this paper we propose a new algorithm for face recognition named as Double Linear Discriminate Analysis (DLDA) in which we combine two linear discriminate methods: 2D-LDA and Fisher-face sequentially. Experimental results on benchmark face datasets show that DLDA not only achieves the best performance when the number of training samples is appropriate, but also its speed is the fastest among...
متن کاملLocally Kernel-based Nonlinear Regression for Face Recognition
The variation of facial appearance due to the viewpoint or pose obviously degrades the accuracy of any face recognition systems. One solution is generating the virtual frontal view from any given non-frontal view to obtain a virtual gallery/probe face. As the state-of-the-art face recognition algorithm, linear regression computes a reconstruction matrix from the images of each subject and then ...
متن کاملLocal Graph Embedding Based on Maximum Margin Criterion (LGE/MMC) for Face Recognition
Locally linear embedding (LLE) is an efficient dimensional reduction algorithm for nonlinear data, and the low dimensional data can maintain topological relations in the original space after the processing. But this algorithm main application is not very good in the data dimensional reduction, the visualization and learning effects of data classification question and so on. In ordered to solve ...
متن کامل