Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.

Face recognition based on PCA and ICA

Abstract: This paper proposes the face recognition method based on principle component analysis (PCA) and independent component analysis (ICA). PCA and ICA are both multivariable data statistics.In order to reduce dimension and sec order correlation,firstly,the image data can be processed by PCA,and then the independent image basis can be got using ICA,finally,face recognition is processed in the subspace. In our experiments,the proposed methods have been successfully evaluated using two different datasets,and PCA and ICA method are compared.The experimental results show that ICA face recognition method is superior to PCA method.

Face Identification based on Biological Trait using Infrared Images after Cold Effect Enhancement and Sunglasses Filtering.

Abstract: This paper describes face identification using infrared images and eigenfaces after passing test face through cold effect enhancement and/or sunglasses filtering algorithms and handling facial hair through threshold. Eigenface technique after modification is used to define our eigenspace. Test image before going through the recognition process has to pass through a check to see whether it is a face image or not. The test face is passed through an algorithm to check and enhance if the person come from cold and then is projected to eigenspace to find the match. If match is not found then it is passed through another algorithm to check whether person has worn sunglasses and if so the image is enhanced in order to make recognition more efficient. Only one person of the test sets is recognized wrongly with the other but both of them belongs to the training set and it gives 100% accurate results for profile images.

Why the alternative PCA provides better performance for face recognition

Abstract: This paper presents an alternative to PCA technique, called as APCA, which uses within class scatter rather than global covariance matrix. The APCA technique produces better features cluster than does common PCA (CPCA) because it keep the null spaces which contain good discriminant information. The proposed technique achieves better performance for both recognition rate and accuracy parameters than those of CPCA when it was tested using several databases (ITS-LAB., INDIA, ORL, and FERET).

Face Recognition Based on Support Vector Machines

Abstract: Face recognition is the research focus of machine vision, pattern recognition and other areas. It has broad application prospects. in this paper, we apply wavelet transform to human face image preprocessing in order to reduce the impact of expression change on face recognition. then we follow PCA method, mapping the original face image to Eigen-faces axis which mutually orthogonal to achieve dimensionality reduction of eigen. Finally we use support vector machine classification model to identify the projection vector of human face image in the eigen faces axis. the experiment results on the ORL and Yale face databases show that the method is feasible.

Robust detection of outliers for projection-based face recognition methods

Abstract: In this paper, the impact of outliers on the performance of high-dimensional data analysis methods is studied in the context of face recognition. Most of the existing face recognition methods are based on PCA-like methods: faces are projected into a lower dimensional space in which similarity between faces is supposed to be more easily evaluated. These methods are, however, very sensitive to the quality of the face images used in the training and in the recognition phases. Their performance significantly drops when face images are not well centered or taken under variable illumination conditions. In this paper, we study this phenomenon for two face recognition methods, namely PCA and LDA2D, and we propose a filtering process that allows the automatic selection of noisy face images which are responsible for the performance degradation. This process uses two techniques. The first one is based on the recently proposed robust high-dimensional data analysis method called RobPCA. It is specific to the case of recognition from video sequences. The second technique is based on a novel and effective face classification technique. It allows isolating still face images that are not very precisely cropped, not well-centered or in a non-frontal pose. Experiments show that this filtering process significantly improves recognition rates by 10 to 30%.