Human Facial Algebraic And Geometrical Feature Extraction And Recognition

Biometric technologies are becoming the foundation of an extensive array of highly secure identification and personal verification solutions. Among the features measured, facial feature identification and verification are gaining popularity and diverse applications for the reason that they are considered to be non-invasive, low cost, and natural biometric technologies.The paper discusses facial feature extraction and recognition from two aspects ?algebraic and geometrical features.The paper discusses the algebraic feature adopting principle component analysis (PCA) and independent component analysis (ICA) respectively. Principle component analysis extracts the image gray characteristics considering the second order moments, while independent component analysis accounts for higher order statistics and identifies the independent source component from their linear mixtures. Considering the differences between them, the paper proposes principle independent component analysis (PICA) that is doing ICA based on PCA. PICA can provide a more powerful classification data representation than PCA by comparing their means and variances.The similarity measurement should match selected features. Most face classification approaches pay more attention to dealing with noise samples, but ignore the influence of selected feature space. Support vector machine (SVM) performs classification by the hyperplane between classes, while the nearest neighbor method (NN) performs classification by the distance between classes. The paper discusses the difference between NN and SVM, finds SVM is more sensitive to the feature space variation than NN, especially for details or noise component. SVM can get better recognition rate only depending on smaller feature space or less approximation components for face recognition.Support vector machine is an advanced classifier, which has demonstrated high generalization capabilities; however, it was developed originally only for two-class classifying. The paper proposes a new multi-step approach to extend SVM capability dealing with the multi-class face recognition problem by incorporating with the elimination strategy. Based on the one-against-one strategy to classify, it sorts the discrimination functions according to their own Vapnik-Chervonenkis confidences and uses the redundancy among them to decrease the discrimination error for the rejecting decision case.The paper proposes a face recognition approach considering the facial algebraic features and the SVMs's classification capability. It includes three basic parts: PCA ?for reducing dimensions, ICA ?for feature extraction and SVM ?for multi-classification. The face recognition experiments with ORL face databaseand a compound database. The recognition rates are 97.5% and 88.17% respectively.The paper discusses the geometric feature points extraction, face and its main parts contour extraction also.It is needed locating the left and the right boundaries of face when extracting geometric feature points. Based on the ordinary intensity level vertical projection curve locating algorithm, the paper employees the reconstructed image by the vertical component of wavelet decomposition of the original image to locating more accurately.The application of fractal theory in image processing shows that fractal image can suppress noise effectively, and reflect the variation of the texture enough. Therefore, the paper substitutes fractal image for intensity image or binary image.The paper proposes the free difference operation, which is a new method computing the variation of gray level, based on the classical differential operators. Free difference operation is not only a general description of some typical differential operators, but also breaks the template mode of classical differential operators. By computing grey level variation along line direction, it considers each pixel feature in the range of the global and local region of image, not the image block or subimage. It has the advantage of simplicity, operational flexibility and better...