Face Recognition using Gaborface-based 2DPCA and (2D)2PCA Classification with Ensemble and Multichannel Model

Conventional multimodal biometrics systems usually do not account for missing data (missing modalities or incomplete score lists) that is commonly encountered in real applications. The presence of missing data in multimodal biometric systems can be inconvenient to the client, as the system will reject the submitted biometric data and request for another trial. In such cases, robust multimodal biometric verification is needed. In this paper, we present the criteria, fusion method and performance metrics of a robust multimodal biometrics verification system that verifies the client's identity at any condition of data missing. A novel adaptive SVM classification method is proposed for missing dimensional values, which can handle the missing data in multimodal biometrics. We show that robust multibiometrics imposes additional requirements on multimodal fusion when compared to conventional multibiometrics. We also argue that the usual performance metrics of false accept and false reject rates are insufficient yardsticks for robust verification and propose new metrics against which we benchmark our system.

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“…Assuming that the are I points in the SPGC curve and J points in the SPIC curve, Average False Rate(AFR) can be defined as follow, 11 11…”

Section: Average Securitymentioning

confidence: 99%

Adaptive SVM fusion for robust multi-biometrics verification with missing data

et al. 2013

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“…Two-dimensional versions of PCA have the advantage of keeping structural information. Wang et al used 2DPCA and (2D) 2 PCA of the Gabor extracted features, with very good results in a face recognition problem [8]. Mutelo et.…”

Section: Introductionmentioning

confidence: 99%

Gabor-based Face Recognition With Illumination Variation Using Subspace-Linear Discriminant Analysis

Kusuma

Wirawan²,

Soeprijanto³

2012

TELKOMNIKA

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Teknik pengenalan wajah telah menjadi topik riset yang aktif dalam beberapa dekade terakhir karena potensi aplikasinya. Pengenalan wajah yang akurat masih merupakan pekerjaan yang sulit, terutama jika iluminasinya tak terbatas. Makalah ini menyajikan suatu metoda yang efisien untuk pengenalan wajah dengan iluminasi yang berbeda-beda menggunakan fitur Gabor, yang diekstraksi dari tapis log-Gabor dengan enam orientasi dan empat skala. Fitur-fitur wajah ini diekstraksi pada daerahdaerah blok pada citra wajah menggunakan algoritma jendela geser. Fitur-fitur hasil ekstraksi kemudian diproses oleh analisis komponen utama (PCA) dan analisis diskriminan linier (LDA). Untuk pengembangan dan pengujian digunakan citra-citra wajah yang berasal dari basis data Yale-B. Metoda yang diusulkan ini menghasilkan laju pengenalan rank 1 sebesar 86-100 %.

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Section: Introductionmentioning

confidence: 99%