(2D)2PCA-ICA: A New Approach for Face Representation and Recognition
نویسندگان
چکیده
In this paper, a new feature extraction algorithm considering both two-directional two-dimensional principal component analysis ((2D)2PCA) and independent component analysis(ICA), called (2D)2PCA-ICA, is proposed for face representation. This algorithm analyzes the principal components of image vectors on 2D matrices by simultaneously considering the row and column directions as opposed to the standard PCA based on 1D vectors, and transforming those principal components to the independent components that maximize the non-Gaussianity of the sources. These two major techniques such as (2D)2PCA and ICA are used sequentially in order to obtain the most efficient features that properly describe a whole set of human faces in face databases. The proposed algorithm is applied to the face recognition problem. Simulation results on ORL and Yale B face databases shows that the proposed algorithm achieves high average success rate in face recognition compared with other models. Keywords—2DPCA, (2D)2PCA, ICA, face recognition
منابع مشابه
2D Dimensionality Reduction Methods without Loss
In this paper, several two-dimensional extensions of principal component analysis (PCA) and linear discriminant analysis (LDA) techniques has been applied in a lossless dimensionality reduction framework, for face recognition application. In this framework, the benefits of dimensionality reduction were used to improve the performance of its predictive model, which was a support vector machine (...
متن کاملA Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network
In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized ...
متن کاملIterative Weighted Non-smooth Non-negative Matrix Factorization for Face Recognition
Non-negative Matrix Factorization (NMF) is a part-based image representation method. It comes from the intuitive idea that entire face image can be constructed by combining several parts. In this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “Iterative weighted non-smooth non-negative matrix factorization” (IWNS-NMF). A new cost fun...
متن کاملHybridization of Facial Features and Use of Multi Modal Information for 3D Face Recognition
Despite of achieving good performance in controlled environment, the conventional 3D face recognition systems still encounter problems in handling the large variations in lighting conditions, facial expression and head pose The humans use the hybrid approach to recognize faces and therefore in this proposed method the human face recognition ability is incorporated by combining global and local ...
متن کاملA New Robust and Discriminating Method for Face Recognition Based on Correlation Technique and Independent Component Analysis Model
We demonstrate a novel technique for face recognition combined the independent component analysis (ICA) model with the optical correlation technique. Our approach relies on the performances of a strongly discriminating optical correlation method along with the robustness of the ICA model. Simulations were performed to illustrate how this algorithm can identify a face with images from the Pointi...
متن کامل