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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Proceedings Article•
01 Jan 2004
TL;DR: This paper makes a new attempt to face recognition based on 3D point clouds by constructing 3D eigenfaces, which describe each mesh model in a lower-dimensional space using the principle component analysis.
Abstract: Face recognition is a very challenging issue and has attracted much attention over the past decades. This paper makes a new attempt to face recognition based on 3D point clouds by constructing 3D eigenfaces. First, a 3D mesh model is built to represent the face shape provided by the point cloud. Then, the principle component analysis (PCA) is used to construct the 3D eigenfaces, which describe each mesh model in a lower-dimensional space. Finally, the nearest neighbor classifier and K-nearest neighbor classifier are utilized for recognition. Experimental results on 3D_RMA, a likely largest 3D face database available currently, show that the proposed algorithm has promising performance with a low computational cost.
42 citations
19 Jun 2001
TL;DR: A novel approach to face recognition based on a multi-pose image sequence is presented, where instead of recognising a face from a single view, a sequence of images showing face movement is used for recognition.
Abstract: A novel approach to face recognition based on a multi-pose image sequence is presented in this paper. In this approach, faces are represented by their pattern vectors (projections to eigenfaces) in eigenspace. Instead of recognising a face from a single view, a sequence of images showing face movement (from left to the right profile) is used for recognition. Pattern vectors corresponding to multiple poses build a trajectory in eigenspace where each trajectory belongs to one face sequence (profile to profile). In the training phase, sequences of poses construct prototype trajectories, and in recognition phase, an unknown face trajectory is compared with prototypes. New matching models are presented and analysed as well as the influence of some parameters on the recognition ratio.
41 citations
01 Jan 2003
TL;DR: A flexible MCS software architecture based on object oriented principles is presented, which allows runtime modifications to the algorithms employed and dynamical selection of classifiers and can be applied to any pattern recognition problem.
Abstract: This paper presents face recognition results obtained using a multi-classifier system (MCS) with Borda count voting. Experiments were conducted on complete sections of the FERET face database with 4 different algorithms: embedded HMM, DCT, EigenFaces and EigenObjects. Particular classifier ensembles yielded almost 6% of improvement over the individual techniques. In order to facilitate experiments on classifier combinations and decision rules comparison, a flexible MCS software architecture based on object oriented principles is also presented. It allows runtime modifications to the algorithms employed and dynamical selection of classifiers. This architecture can be applied to any pattern recognition problem.
41 citations
01 May 2011
TL;DR: This system provides an end-to-end solution for face recognition, it receives video input from a camera, detects the locations of the face(s) using the Viola-Jones algorithm, subsequently recognizes each face using the Eigenface algorithm, and outputs the results to a display.
Abstract: Face recognition systems play a vital role in many applications including surveillance, biometrics and security. In this work, we present a {\textit complete} real-time face recognition system consisting of a face detection, a recognition and a down sampling module using an FPGA. Our system provides an end-to-end solution for face recognition, it receives video input from a camera, detects the locations of the face(s) using the Viola-Jones algorithm, subsequently recognizes each face using the Eigenface algorithm, and outputs the results to a display. Experimental results show that our complete face recognition system operates at 45 frames per second on a Virtex-5 FPGA.
41 citations
26 Dec 2007
TL;DR: The "Sparse LDA" algorithm is extended with new sparsity bounds on 2-class separability and efficient partitioned matrix inverse techniques leading to 1000-fold speed-ups and state-of-the-art recognition is obtained while discarding the majority of pixels in all experiments.
Abstract: We extend the "Sparse LDA" algorithm of [7] with new sparsity bounds on 2-class separability and efficient partitioned matrix inverse techniques leading to 1000-fold speed-ups. This mitigates the 0(n4) scaling that has limited this algorithm's applicability to vision problems and also prioritizes the less-myopic backward elimination stage by making it faster than forward selection. Experiments include "sparse eigenfaces" and gender classification on FERET data as well as pixel/part selection for OCR on MNIST data using Bayesian (GP) classification. Sparse- LDA is an attractive alternative to the more demanding Automatic Relevance Determination. State-of-the-art recognition is obtained while discarding the majority of pixels in all experiments. Our sparse models also show a better fit to data in terms of the "evidence" or marginal likelihood.
40 citations