Eigenface

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

Face Recognition using Principle Component Analysis

Abstract: Their system tries to detect the critical areas of the face. The system is based on matching the image to a map of invariant facial attributes associated with specific areas of the face. PCA: The proposed system is based on an information theory approach that decomposes face images into a small set of characteristic feature images called „Eigen faces‟, which are actually the principal components of the initial training set of face images. Recognition is performed by projecting a new image into the subspace spanned by the Eigen faces („face space‟) and then classifying the face by comparing its position in the face space with the positions of the known individuals. The Eigen face approach gives us efficient way to find this lower dimensional space. Eigen faces are the Eigenvectors which are representative of each of the dimensions of this face space and they can be considered as various face features. Any face can be expressed as linear combinations of the singular vectors of the set of faces, and these singular vectors are eigenvectors of the covariance matrices.

An Introduction to Face Recognition Technology

Abstract: Introduction In today's networked world, the need to maintain the security of information or physical property is becoming both increasingly important and increasingly difficult. From time to time we hear about the crimes of credit card fraud, computer break-in's by hackers, or security breaches in a company or government building. In the year 1998, sophisticated cyber crooks caused well over US $100 million in losses (Reuters, 1999). In most of these crimes, the criminals were taking advantage of a fundamental flaw in the conventional access control systems: the systems do not grant access by "who we are", but by "what we have", such as ID cards, keys, passwords, PIN numbers, or mother's maiden name. None of these means are really define us. Rather, they merely are means to authenticate us. It goes without saying that if someone steals, duplicates, or acquires these identity means, he or she will be able to access our data or our personal property any time they want. Recently, technology became available to allow verification of "true" individual identity. This technology is based in a field called "biometrics". Biometric access control are automated methods of verifying or recognizing the identity of a living person on the basis of some physiological characteristics, such as fingerprints or facial features, or some aspects of the person's behavior, like his/her handwriting style or keystroke patterns. Since biometric systems identify a person by biological characteristics, they are difficult to forge. Among the various biometric ID methods, the physiological methods (fingerprint, face, DNA) are more stable than methods in behavioral category (keystroke, voice print). The reason is that physiological features are often non-alterable except by severe injury. The behavioral patterns, on the other hand, may fluctuate due to stress, fatigue, or illness. However, behavioral IDs have the advantage of being non-intrusiveness. People are more comfortable signing their names or speaking to a microphone than placing their eyes before a scanner or giving a drop of blood for DNA sequencing. Face recognition is one of the few biometric methods that possess the merits of both high accuracy and low intrusiveness. It has the accuracy of a physiological approach without being intrusive. For this reason, since the early 70's (Kelly, 1970), face recognition has drawn the attention of researchers in fields from security, psychology, and image processing, to computer vision. Numerous algorithms have been proposed for face recognition; for detailed survey please see Chellappa (1995) and Zhang (1997). While network security and access control are it most widely discussed applications, face recognition has also proven useful in other multimedia information processing areas. Chan et al. (1998) use face recognition techniques to browse video database to find out shots of particular people. Li et al. (1993) code the face images with a compact parameterized facial model for low-bandwidth communication applications such as videophone and teleconferencing. Recently, as the technology has matured, commercial products (such as Miros' TrueFace (1999) and Visionics' FaceIt (1999)) have appeared on the market. Despite the commercial success of those face recognition products, a few research issues remain to be explored. In the next section, we will begin our study of face recognition by discussing several metrics to evaluate the recognition performance. Section 3 provides a framework for a generic face recognition algorithm. Then in Section 4 we discuss the various factors that affect the performance of the face recognition system. In section 5, we show the readers several famous face recognition examples, such as eigenface and neural network. Then finally a conclusion is given in section 6. Performance Evaluation Metrics The two standard biometric measures to indicate the identifying power are False Rejection Rate (FRR) and False Acceptance Rate (FAR). …

Face recognition using range images

Abstract: A system for face recognition using range images as input data is described. The range data acquisition procedure is based on the coded light approach, merging range images that are recorded by two separate sensors. Two approaches, which are known from face recognition based on grey level images have been extended to dealing with range images. These approaches are based on eigenfaces and hidden Markov models, respectively. Experimental results on a database with various range images from 24 persons show very promising results for both recognition methods.