Face recognition using nonparametric-weighted Fisherfaces

This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE is a powerful tool for extracting hyperspectral image features. The Fisherfaces method maximizes the ratio of between-class scatter to that of within-class scatter. In this study, the proposed NW-Fisherfaces weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face. The proposed NW-Fisherfaces was compared with Orthogonal Laplacianfaces, Eigenfaces, Fisherfaces, direct linear discriminant analysis, and null space linear discriminant analysis methods for tests on five facial databases. Experimental results showed that the proposed approach outperforms other feature extraction methods for most databases. © 2012 Li et al

Multi-agent system for selecting images based on the gender and age

This paper presents a multi-agent system that is able to search people on a database of images recognizing patterns of facial features on each person, based on the main features of the face (eyes, nose and mouth). Using that multi-agent architecture, the system can do the work faster applying Fisherfaces algorithm for the face recognition and classification. This technology can be used for several purposes like specific ads in each user group to suit better their interests or search for the age and gender of people that usually go to different places like malls or shops

Non-parametric Ensemble Kalman methods for the inpainting of noisy dynamic textures

International audienceIn this work, we propose a novel non parametric method for the temporally consistent inpainting of dynamic texture sequences. The inpainting of texture image sequences is stated as a stochastic assimilation issue, for which a novel model-free and data-driven Ensemble Kalman method is introduced. Our model is inspired by the Analog Ensemble Kalman Filter (AnEnKF) recently proposed for the assimilation of geophysical space-time dynamics, where the physical model is replaced by the use of statistical analogs or nearest neighbours. Such a non-parametric framework is of key interest for image processing applications, as prior models are seldom available in general. We present experimental evidence for real dynamic texture that using only a catalog database of historical data and without having any assumption on the model, the proposed method provides relevant dynamically-consistent interpolation and outperforms the classical parametric (autoregressive) dynamical prior

A comparative analysis of neural and statistical classifiers for dimensionality reduction-based face recognition systems.

Human face recognition has received a wide range of attention since 1990s. Recent approaches focus on a combination of dimensionality reduction-based feature extraction algorithms and various types of classifiers. This thesis provides an in depth comparative analysis of neural and statistical classifiers by combining them with existing dimensionality reduction-based algorithms. A set of unified face recognition systems were established for evaluating alternate combinations in terms of recognition performance, processing time, and conditions to achieve certain performance levels. A preprocessing system and four dimensionality reduction-based methods based on Principal Component Analysis (PCA), Two-dimensional PCA, Fisher\u27s Linear Discriminant and Laplacianfaces were utilized and implemented. Classification was achieved by using various types of classifiers including Euclidean Distance, MLP neural network, K-nearest-neighborhood classifier and Fuzzy K-Nearest Neighbor classifier. The statistical model is relatively simple and requires less computation complexity and storage. Experimental results were shown after the algorithms were tested on two databases of known individuals, Yale and AR database. After comparing these algorithms in every aspect, the results of the simulations showed that considering recognition rates, generalization ability, classification performance, the power of noise immunity and processing time, the best results were obtained with the Laplacianfaces, using either Fuzzy K-NN.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .X86. Source: Masters Abstracts International, Volume: 45-01, page: 0428. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006

A Comprehensive Review on Face detection using Machine learning classification

Increased interest in biometric security systems makes face recognition a very effective research area. Now, we will take a look on basics of a face recognition system. In an image a face region is present where only facial features are present. In another terms it means we have to localize or concentrate only on the face region that means we are contemplating those parts of an image where a face may present. This research provides an up-to date survey of various existing recognition techniques but also represent precise descriptions of some methods. In addition to this other topics like issues of illumination and pose variation are also discussed in their work. The purpose of this short review paper is to present, categorize and evaluate some new face detection techniques using four conventional learning machine. The performance and the other evaluation parameters of these methods compare with each other in order to introduce significant techniques and also to state advantages and disadvantages of related work