Eigenface

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

A reliable approach for human face detection using genetic algorithm

Abstract: In this paper, a reliable method for detecting human faces in an image is devised. The approach is based on the genetic algorithm and the eigenface technique. As the genetic algorithm is a computationally intensive process, the searching space for possible face regions is limited to possible eye regions so that the required timing is greatly reduced. In addition, the lighting effect and orientation of the faces are considered and solved in this method.

Performance evaluation of incremental training method for face recognition using PCA

Abstract: Relevance of ‘face recognition’ (FR) in the modern world requirements is presented as a case of human machine interaction. Physical conditions that influence the face recognition process regarding the facial features, illumination changes and viewing angles etc. are discussed. Face recognition process predominantly depends on machine perception i.e. information through an array of pixels with respect to the facial image. Details of eigenface approach through the involvement of contemporary algebraic and statistical analysis are revisited. Methodology involved in the Principal Component Analysis and advantages of exposing the data to incremental training (using PCA) are discussed. A model for the implementation of IPCA over the face databases is proposed to estimate its performance for the face recognition process. Performance of the present model is studied in the domain of Euclidean distance, decay parameter, recognition rate, eigenvalues and overall computational time. Present IPCA model administered over standard ORL, FERET databases along with that over the JNTU face database with large number of face images revealed relative performance. The merit of present IPCA is inferred through enhanced recognition rate and reduced complexity (in the algorithm), intelligent eigenvectors and lesser computational time. The results are presented in the wake of the body of data available with other methods.

Multiple face recognition in real-time using cloud computing, Emgu CV and Windows Azure

Abstract: Multiple face recognition has several applications, such as in the areas of security and robotics. Recognition and classification techniques have been developed in recent years, through different programming languages and approaches. However, the level of detailing often requires a high processing power. This article proposes the use of cloud computing - more specifically, Windows Azure platform - to identify possible performance gains while testing EmguCV framework.

Face detection and recognition in videos

Abstract: Advancement in computer technology has made possible to evoke new video processing applications in field of biometric face detection and recognition. It has wide range of applications in human recognition, human computer interaction (HCI), behavior analysis, teleconferencing and video surveillance. Face is vital part of human anatomy that reflects prominent topographies of a person. Face detection has become popular biometric trait in recent years due to its importance in security control applications. The first step in practical face analysis systems is real-time detection of face in frames containing face and complex objects in background. In this paper a system is proposed for human face detection using Haar features and recognition using Eigen and Gabor filter in videos. Efforts are made to minimize processing time for detection and recognition process. The Eigenface method performs well in terms of computational complexity whereas Gabor filter are robust to pose changes.

Bi-2DPCA: A Fast Face Coding Method for Recognition

Abstract: Face recognition has received significant attention in the past decades due to its potential applications in biometrics, information security, law enforcement, etc. Numerous methods have been suggested to address this problem [1]. Among appearance-based holistic approaches, principal component analysis (PCA) turns out to be very effective. As a classical unsupervised learning and data analysis technique, PCA was first used to represent images of human faces by Sirovich and Kirby in 1987 [2, 3]. Subsequently, Turk and Pentland [4, 5] applied PCA to face recognition and presented the well-known Eigenfaces method in 1991. Since then, PCA has been widely investigated and has become one of the most successful approaches to face recognition [6-15]. PCA-based image representation and analysis technique is based on image vectors. That is, before applying PCA, the given 2D image matrices must be mapped into 1D image vectors by stacking their columns (or rows). The resulting image vectors generally lead to a highdimensional image vector space. In such a space, calculating the eigenvectors of the covariance matrix is a critical problem deserving consideration. When the number of training samples is smaller than the dimension of images, the singular value decomposition (SVD) technique is useful for reducing the computational complexity [1-4]. However, when the training sample size becomes large, the SVD technique is helpless. To deal with this problem, an incremental principal component analysis (IPCA) technique has been proposed recently [16]. But, the efficiency of this algorithm still depends on the distribution of data. Over the last few years, two PCA-related methods, independent component analysis (ICA) [17] and kernel principal component analysis (KPCA) [18, 19] have been of wide concern. Bartlett [20], Yuen [21], Liu [22], and Draper [23] proposed using ICA for face representation and found that it was better than PCA when cosine was used as the similarity measure (however, the performance difference between ICA and PCA was not significant if the Euclidean distance is used [23]). Yang [24] and Liu [25] used KPCA for face feature extraction and recognition and showed that KPCA outperforms the classical PCA. Like PCA, ICA and KPCA both follow the matrix-to-vector mapping strategy when they are used for image analysis and, their algorithms are more complex than PCA. So, ICA and KPCA are considered to be computationally more expensive than PCA. The experimental results in 16