Eigenface

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

Automatic Face Recognition: What Representation?

Abstract: A testbed for automatic face recognition shows an eigenface coding of shape-free texture, with manually coded landmarks, was more effective than correctly shaped faces, being dependent upon high-quality representation of the facial variation by a shape-free ensemble. Configuration also allowed recognition, these measures combine to improve performance and allowed automatic measurement of the face-shape. Caricaturing further increased performance. Correlation of contours of shapefree images also increased recognition, suggesting extra information was available. A natural model considers faces as in a manifold, linearly approximated by the two factors, with a separate system for local features.

Enhanced Face Recognition Algorithm using PCA with Artificial Neural Networks

Abstract: Face detection from a long database of face images with different backgrounds is not an easy task. In this work, we demonstrate the face detection system of colored face images which is invariant to the background and acceptable illumination conditions. A threshold level is set to reject the non-human face images and the unknown human face images which are not present in the input database of face images. In this paper, the global features extraction is completed using PCA based eigenface computation method and the detection part is completed using multi-layered feed forward Artificial Neural Networks with back propagation process. This algorithm is implemented using MATLAB software. The learning process of neurons is used to train the input face images with 1000 iterations to minimize the error. In this system, face recognition task is completed with improved accuracy and success rate even for noisy face images. Face Recognition System is a computer based digital technology and is an active area of research. The Face Recognition System has various applications like various authentication systems, security systems and searching of persons etc. These applications are cost effective and save the time. Moreover the face database can be easily designed by using any image of the person. In past few years various face recognition techniques are purposed with varied and successful results. As the brain of human beings create the learning ability to recognize the persons by face even the feature characteristics of the face changes with time. The neurons of the human brain are trained by reading or learning the face of a person and they can identify that face quickly even after several years. This ability of training and identifying is converted into machine systems using the Artificial Neural Networks. The basic function for the face recognition system is to compare the face of a person which is to be recognized with the faces already trained in the Artificial Neural Networks and it recognized the best matching face as output even at different lightening conditions, viewing conditions and facial expressions. In this paper, the features of the face images are extracted by creating the feature vectors of maximum varied face points and computing s Covariance column matrix using PCA. These faces are projected onto the face space that spans the significant variations in the face images stored in the database (7). These feature vectors are the eigenvectors of covariance matrix and having the face like appearance so that we call them eigenfaces which are used as input to train the Artificial Neural Networks. The learning of the correlated patterns between the input face images is one of the useful properties of Artificial Neural Networks. After training the Artificial Neural Networks, we tested it with known and unknown face images for success and rejection rate analysis. Database used in this work contains 49 different face images of nine persons resized to 180×200 pixels including the non-human and unknown face images for improving the rejection rate.

Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition

Abstract: We introduce a novel formalism that performs dimensionality reduction and captures topological features (such as the shape of the observed data) to conduct pattern classification. This mission is achieved by: 1) reducing the dimension of the observed variables through a kernelized radial basis function technique and expressing the latent variables probability distribution in terms of the observed variables; 2) disclosing the data manifold as a 3-D polyhedron via the $\alpha $ -shape constructor and extracting topological features; and 3) classifying a data set using a mixture of multinomial distributions. We have applied our methodology to the problem of age-invariant face recognition. Experimental results obtained demonstrate the efficiency of the proposed methodology named nonlinear topological component analysis when compared with some state-of-the-art approaches.

Automatic Attendance Management System Using Face Recognition

Abstract: Being one of the most successful applications of the image processing, face recognition has a vital role in technical field especially in the field of security purpose. Human face recognition is an important field for verification purpose especially in the case of student's attendance. This paper is aimed at implementing a digitized system for attendance recording. Current attendance marking methods are monotonous & time consuming. Manually recorded attendance can be easily manipulated. Hence the paper is proposed to tackle all these issues. extraction methods viz. PCA (Principal Component Analysis) -Thus we create a feature set for each of the images provided in the database. During real time, the images of human face may be extracted from a USB camera. This involves MATLAB's Image Acquisition Toolbox, using which a camera is configured, accessed & brought one frame at a time into MATLAB's workspace for further processing using MATLAB's Image Processing Toolbox. This method uses eigen face approach for face recognition which was introduced by Kirby and Sirovich in 1988 at Brown University. The method works by analyzing face images and computing eigenface which are faces composed of eigenvectors. The comparison of eigenface is used to identify the presence of a face and its identity. There is a five step process involved with the system developed byTurk and

Performance improvement for face recognition using PCA and two-dimensional PCA

Abstract: Now a days research is going on to design a high performance automatic face recognition system which is really a challenging task for researchers. As faces are complex visual stimuli that differ dramatically, hence developing an efficient computational approach for accurate face recognition is very difficult. In this paper a high performance face recognition algorithm is developed and tested using conventional Principal Component Analysis (PCA) and two dimensional Principal Component Analysis (2DPCA). These statistical transforms are exploited for feature extraction and data reduction. We have proposed here to assign different weight to the only very few nonzero eigenvalues related eigenvectors which are considered as non-trivial principal components for classification. Lastly face recognition task is performed by k-nearest distance measurement. Experimental results on ORL and YALE face databases show that the proposed method improves the performance of face recognition with respect to existing techniques. The results show that better recognition performance can be achieved with less computational cost than that of other existing methods.