Topic
Eigenface
About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.
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TL;DR: The results show that the proposed approach is highly discriminant and achieves a promising accuracy for face recognition than the state-of-the-art approaches.
Abstract: Face recognition is an interesting and a challenging problem that has been widely studied in the eld of pattern recognition and computer vision. It has many applications such as biometric authentication, video surveillance, and others. In the past decade, several methods for face recognition were proposed. However, these methods suffer from pose and illumination variations. In order to address these problems, this paper proposes a novel methodology to recognize the face images. Since image gradients are invariant to illuminationandposevariations,theproposedapproachusesgradientorientationtohandletheseeffects.The Schur decomposition is used for matrix decomposition and then Schurvalues and Schurvectors are extracted for subspaceprojection. We call thissubspace projection offace features asSchurfaces, which isnumerically stable and have the ability of handling defective matrices. The Hausdorff distance is used with the nearest neighbor classier to measure the similarity between different faces. Experiments are conducted with Yale face database and ORL face database. The results show that the proposed approach is highly discriminant and achieves a promising accuracy for face recognition than the state-of-the-art approaches.
40 citations
TL;DR: Algorithms to assess the quality of facial images affected by factors such as blurriness, lighting conditions, head pose variations, and facial expressions, based upon the eigenface technique are presented.
Abstract: In this paper, we presented algorithms to assess the quality of facial images affected by factors such as blurriness, lighting conditions, head pose variations, and facial expressions. We developed face recognition prediction functions for images affected by blurriness, lighting conditions, and head pose variations based upon the eigenface technique. We also developed a classifier for images affected by facial expressions to assess their quality for recognition by the eigenface technique. Our experiments using different facial image databases showed that our algorithms are capable of assessing the quality of facial images. These algorithms could be used in a module for facial image quality assessment in a face recognition system. In the future, we will integrate the different measures of image quality to produce a single measure that indicates the overall quality of a face image
40 citations
01 Dec 2008
TL;DR: A new image representation method is proposed for face recognition in this paper, called local Gabor phase difference pattern (LGPDP), where the feature encodes the discriminative information in an elaborate way to avoid the sensitivity of the Gaborphase to location variations.
Abstract: A new image representation method is proposed for face recognition in this paper, called local Gabor phase difference pattern (LGPDP). Unlike the Histogram of Gabor Phase Patterns (HGPP) that exploits the relationships of Gabor phase between neighborhood pixels, the LGPDP captures the Gabor phase difference relationships to represent the image. In order to avoid the sensitivity of the Gabor phase to location variations, the feature encodes the discriminative information in an elaborate way. The impressive experimental results of the proposed method compared with some other state-of-art methods conducted on the FERET database and the ORL face database demonstrate its efficiency and validity.
40 citations
26 Mar 2000
TL;DR: The experimental results indicate that the DSW approach outperforms the eigenface approach in both cases, and the basic idea of the algorithm is to combine local features under certain spatial constraints.
Abstract: We investigate the recognition of human faces in a meeting room. The major challenges of identifying human faces in this environment include low quality of input images, poor illumination, unrestricted head poses and continuously changing facial expressions and occlusion. In order to address these problems we propose a novel algorithm, dynamic space warping (DSW). The basic idea of the algorithm is to combine local features under certain spatial constraints. We compare DSW with the eigenface approach on data collected from various meetings. We have tested both front and profile face images and images with two stages of occlusion. The experimental results indicate that the DSW approach outperforms the eigenface approach in both cases.
40 citations
01 Jan 2014
TL;DR: It is concluded that PCA is a good technique for face recognition as it is able to identify faces fairly well with varying illuminations, facial expressions etc.
Abstract: The face being the primary focus of attention in social interaction plays a major role in conveying identity and emotion. A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. The main aim of this paper is to analyse the method of Principal Component Analysis (PCA) and its performance when applied to face recognition. This algorithm creates a subspace (face space) where the faces in a database are represented using a reduced number of features called feature vectors. The PCA technique has also been used to identify various facial expressions such as happy, sad, neutral, anger, disgust, fear etc. Experimental results that follow show that PCA based methods provide better face recognition with reasonably low error rates. From the paper, we conclude that PCA is a good technique for face recognition as it is able to identify faces fairly well with varying illuminations, facial expressions etc.
40 citations