Selection of optimized features and weights on face-iris fusion using distance images

Eskandari, Maryam; Toygar, Önsen

doi:10.1016/j.cviu.2015.02.011

Cited by 52 publications

(29 citation statements)

References 36 publications

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“…A new method has been proposed in [20] to fuse face and iris biometric traits with the weighted score level fusion technique to flexibly fuse the matching scores from these two modalities based on their weights availability. A more recent scheme has been proposed by [21], which uses matching score level and feature level fusion combination to improve the face and iris multimodal biometric systems. Optimized Weighted Sum Rule fusion has been applied in their work for score level fusion along with feature selection techniques such as Particle Swarm Optimization (PSO) and the Backtracking Search Algorithm (BSA) at feature level fusion.…”

Section: Introductionmentioning

confidence: 99%

Optimal Face-Iris Multimodal Fusion Scheme

Sharifi

Eskandari

2016

Symmetry

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Multimodal biometric systems are considered a way to minimize the limitations raised by single traits. This paper proposes new schemes based on score level, feature level and decision level fusion to efficiently fuse face and iris modalities. Log-Gabor transformation is applied as the feature extraction method on face and iris modalities. At each level of fusion, different schemes are proposed to improve the recognition performance and, finally, a combination of schemes at different fusion levels constructs an optimized and robust scheme. In this study, CASIA Iris Distance database is used to examine the robustness of all unimodal and multimodal schemes. In addition, Backtracking Search Algorithm (BSA), a novel population-based iterative evolutionary algorithm, is applied to improve the recognition accuracy of schemes by reducing the number of features and selecting the optimized weights for feature level and score level fusion, respectively. Experimental results on verification rates demonstrate a significant improvement of proposed fusion schemes over unimodal and multimodal fusion methods.

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Section: Introductionmentioning

confidence: 99%

Optimal Face-Iris Multimodal Fusion Scheme

Sharifi

Eskandari

2016

Symmetry

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“…On the other hand, a texture-based approach such as local binary patterns (LBP) is used to find robust local features that are invariant to pose or illumination variation. LBP algorithm is used in many recognition/classification problems (Eskandari & Toygar, 2015;Farmanbar & Toygar, 2015;Kalyoncu & Toygar, 2016) as well as in face and ear recognition. In Mahmood, Ali, and Khan (2016), the adaptive boosting with linear discriminant analysis as weak learner, the PCA-based approach, and LBP-based approach were used to analyse the effect of poses and image resolutions on face recognition.…”

Section: Related Workmentioning

confidence: 99%

On the use of ear and profile faces for distinguishing identical twins and nontwins

2019

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This study aims to measure the efficiency of ear and profile face in distinguishing identical twins under identification and verification modes. In addition, to distinguish identical twins by ear and profile face separately, we propose to fuse these traits with all possible binary combinations of left ear, left profile face, right ear, and right profile face. Fusion is implemented by score‐level fusion and decision‐level fusion techniques in the proposed method. Additionally, feature‐level fusion is used for comparison. All experiments in this paper are also implemented on nontwins individuals, and the recognition performance of twins and nontwins are compared. Local binary patterns, local phase quantization, and binarized statistical image features approaches are used as texture‐based descriptors for feature extraction process. Images under controlled and uncontrolled lighting are tested. Ear and profile images from ND‐TWINS‐2009‐2010 dataset are used in the experiments. The experimental results show that the proposed method is more accurate and reliable than using ear or profile face images separately. The performance of the proposed method for recognizing identical twins as recognition rate is 100% and 99.45%, and equal error rates are 0.54% and 1.63% in controlled and uncontrolled illumination conditions, respectively.

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“…The fusion of two or more biometric systems can be performed at data level, feature level, match score level and decision level. 8,33 Features extracted from biometric modalities have a rich source of information and fusing features allows classes to be more separable leading to improve the performance. The two most widely used fusion strategies in the literature are feature level and matching score level fusion.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Person Identification Using Palmprint and Face Biometrics

Farmanbar

Toygar

2015

Int. J. Patt. Recogn. Artif. Intell.

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This paper proposes hybrid approaches based on both feature level and score level fusion strategies to provide a robust recognition system against the distortions of individual modalities. In order to compare the proposed schemes, a virtual multimodal database is formed from FERET face and PolyU palmprint databases. The proposed hybrid systems concatenate features extracted by local and global feature extraction methods such as Local Binary Patterns, Log Gabor, Principal Component Analysis and Linear Discriminant Analysis. Match score level fusion is performed in order to show the e®ectiveness and accuracy of the proposed schemes. The experimental results based on these databases reported a signi¯cant improvement of the proposed schemes compared with unimodal systems and other multimodal face-palmprint fusion methods.

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Selection of optimized features and weights on face-iris fusion using distance images

Cited by 52 publications

References 36 publications

Optimal Face-Iris Multimodal Fusion Scheme

Optimal Face-Iris Multimodal Fusion Scheme

On the use of ear and profile faces for distinguishing identical twins and nontwins

A Hybrid Approach for Person Identification Using Palmprint and Face Biometrics

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