The Complete Gabor-Fisher Classifier for Robust Face Recognition

Štruc, Vitomir; Pavešić, Nikola

doi:10.1155/2010/847680

Cited by 118 publications

(81 citation statements)

References 46 publications

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“…The performance measures were calculated by using the MATLAB PhD (Pretty Helpful Development) toolbox for face recognition [31,32].…”

Section: Resultsmentioning

confidence: 99%

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Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis

2016

J Inf Process Syst

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The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.

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“…The performance measures were calculated by using the MATLAB PhD (Pretty Helpful Development) toolbox for face recognition [31,32].…”

Section: Resultsmentioning

confidence: 99%

“…− As an example, the first row (a) in Fig. 8 displays four face images of the same person at different ages (24,31,42, and 61 years old) after normalization. The second row (b) in Fig.…”

Section: M×n Dimensionmentioning

confidence: 99%

Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis

2016

J Inf Process Syst

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show abstract

“…Firstly and prior to extract the feature, a histogram equalization and photometric normalization procedure that normalized face images from the database to zero mean and unit variance was used. Secondly, the method called 'Phasebased Gabor Fisher Classifier (PBGFC)' by Struc [35] was used to extract of features, while the linear discriminant analysis (LDA) was employed in the second step to project the augmented feature vectors into a low dimensional space. Finally, the cosine Mahalanobis distance similarity measure was employed for recognition.…”

Section: Face Recognition Systemmentioning

confidence: 99%

“…For feature extraction, we adopted the method proposed by Struc [35] called 'Phase-based Gabor Fisher Classifier (PBGFC)' which has given the best results with 2 scales and eight orientations. This method is really robust in varying illumination conditions and it significantly reduces the computational burden required for extraction of the facial features.…”

Section: A Features Extractionmentioning

confidence: 99%

A Robust Multi-Biometric System with Compact Code for Iris and Face

Nouar¹,

Taleb²

2018

IJEEI

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Multimodal biometric can overcome the limitations of the single biometric trait and gives better classification accuracy. This paper proposes a face-iris multimodal biometric system based on fusion at the matching score level. The iris recognition system is composed of segmentation, normalization, feature encoding and matching. The wavelet transform is used in feature extraction to generate a compact feature vector length of 128 bits; this reduces the computational time and storage of the iris code. The face recognition system is composed of enhancement, feature encoding and matching. The new method called 'Phase-based Gabor Fisher Classifier (PBGFC)' is used in feature extraction; PBGFC employs only 16 Gabor filters, i.e., filters with 2 scales and eight orientations. This fact makes the resulting feature vector for the PBGFC method very compact. The scores from iris and face are then combined using several score normalization and fusion techniques. To validate our approach, experiments are conducted on the iris and face images obtained from the CASIA and ORL datasets respectively. The results show that our multimodal biometric system achieves higher accuracy than both single biometric approaches and the other existing multi-biometric systems based on fusion of iris and face.

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“…In wavelet analysis the discrete wavelet transform (DWT) [10] and Gabor wavelet transform (GWT) [11]- [12] based face recognition methods are more popular. GWT is more successful due to its capability of effectively expressing face feature for its directional selectivity.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Face Recognition Performance using Triplet Half Band Wavelet Filter Bank

Muqeet¹,

Holambe²

2016

IJIGSP

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Abstract-Face recognition using subspace methods are quite popular in research community. This paper proposes an efficient face recognition method based on the application of recently developed triplet half band wavelet filter bank (TWFB) as pre-processing step to further enhance the performance of well known linear and nonlinear subspace methods such as principle component analysis(PCA),kernel principle component analysis (KPCA), linear discriminant analysis (LDA), and kernel discriminant analysis (KDA). The design of 6 th order TWFB is used as the multiresolution analysis tool to perform the 2-D discrete wavelet transform (DWT). Experimental results are performed on two standard databases ORL and Yale. Comparative results are obtained in terms of verification performance parameters such as false acceptance rate (FAR), false rejection rate (FRR) and genuine acceptance rate (GAR). Application of TWFB enhances the performance of PCA, KPCA, LDA, and KDA based methods.

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The Complete Gabor-Fisher Classifier for Robust Face Recognition

Cited by 118 publications

References 46 publications

Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis

Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis

A Robust Multi-Biometric System with Compact Code for Iris and Face

Enhancing Face Recognition Performance using Triplet Half Band Wavelet Filter Bank

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