Combined Classifiers for Invariant Face Recognition

Tolba, A. S.; Abu-Rezq, Abdulnasser

doi:10.1007/s100440070001

Cited by 40 publications

(8 citation statements)

References 38 publications

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“…These three datasets have their own characteristics in evaluating the performances of different face recognition algorithms [25] [26]. We choose three benchmark datasets for evaluation in our experiments.…”

Section: Resultsmentioning

confidence: 99%

Face Recognition via Two Dimensional Locality Preserving Projection in Frequency Domain

Liu²,

Liu

2010

Lecture Notes in Computer Science

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In this paper we investigate the face recognition problem via using the two dimensional locality preserving projection in frequency domain. For this purpose, we first introduce the two-dimensional locality preserving projections (2DLPP) and the two dimensional discrete cosine transform (2DDCT). Then the 2DLPP in frequency domain is proposed for face recognition. In fact, the 2DDCT is used as a pre-processing step and it converts the image signal from time domain into frequency domain aiming to reduce the effects of illumination and pose on face recognition. Then 2DLPP is applied on the upper left corner blocks of the global 2DDCT transform matrices of the original images, which represent the central energy of original images. For demonstration, the Olivetti Research Laboratory (ORL), YALE and FERET face datasets are used to compare the proposed approach with the conventional 2DLPP and 2DDCT approaches with the nearest neighborhood (NN) metric being used for classifiers. The experimental results show that the proposed 2DLPP in frequency domain is superior over the 2DLPP in time domain and 2DDCT in frequency domain.

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Section: Resultsmentioning

confidence: 99%

Face Recognition via Two Dimensional Locality Preserving Projection in Frequency Domain

Liu²,

Liu

2010

Lecture Notes in Computer Science

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“…Most of such work focuses on fusing 'weak' classifiers for the purpose of increasing the overall performance (Tolba & Rezq, 2000) [3]. A hybrid fingerprint matcher [4] which fuses minutiae and reference point location classifiers has been proposed by Ross, Jain & Riesman (2003).…”

Section: Fusion At the Decision Levelmentioning

confidence: 99%

“…This problem can be solved by installing multiple sensors that capture different biometric traits. Such systems, known as multimodal biometric systems [3], are expected to be more reliable due to the presence of multiple pieces of evidence. These systems are also able to meet the stringent performance requirements imposed by various applications.…”

Section: Introductionmentioning

confidence: 99%

Multimodal biometrics system for efficient human recognition

et al. 2006

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This paper proposes the multimodal biometrics system for identity verification using four traits i.e., face, fingerprint, iris and signature. The proposed system is designed for applications where the training database contains a face, iris, two fingerprint images and/or one or two signature image(s) for each individual. The final decision is made by fusion at "matching score level architecture" in which feature vectors are created independently for query images and are then compared to the enrollment templates which are stored during database preparation for each biometric trait. Based on the proximity of feature vector and template, each subsystem computes its own matching score. These individual scores are finally combined into a total score, which is passed to the decision module. Multimodal system is developed through fusion of face, fingerprint, iris and signature recognition. This system is tested on IITK database and the overall accuracy of the system is found to be more than 97% accurate with FAR and FRR of 2.46% and 1.23% respectively.

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“…[14][15][16][17] In particular, it has been reported that different appearance-based methods, like PCA and LDA, show different performances on different face images (i.e. on images with different face appearances and environmental conditions), so suggesting that PCA and LDA can contribute complementary information to the face recognition task.…”

Section: Introductionmentioning

confidence: 99%

Decision-Level Fusion of Pca and Lda-Based Face Recognition Algorithms

Marcialis

Roli

2006

Int. J. Image Grap.

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In this paper, a face recognition system based on the fusion of two well-known appearance-based algorithms, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), is proposed. Fusion is performed at the decision-level, that is, the outputs of the individual face recognition algorithms are combined. Two main benefits of such fusion are shown. First, the reduction of the dependence on the environmental conditions with respect to the best individual recognizer. Secondly, the overall performance improvement over the best individual recognizer. To this end, fusion is investigated under different environmental conditions, namely, "ideal" conditions, characterized by a very limited variability of environmental parameters, and "real" conditions with large variability of lighting and face expressions.

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Combined Classifiers for Invariant Face Recognition

Cited by 40 publications

References 38 publications

Face Recognition via Two Dimensional Locality Preserving Projection in Frequency Domain

Face Recognition via Two Dimensional Locality Preserving Projection in Frequency Domain

Multimodal biometrics system for efficient human recognition

Decision-Level Fusion of Pca and Lda-Based Face Recognition Algorithms

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