AAM-based palm segmentation in unrestricted backgrounds and various postures for palmprint recognition

Aykut, Murat; Ekinci, Murat

doi:10.1016/j.patrec.2013.02.016

Cited by 34 publications

(21 citation statements)

References 20 publications

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“…The ROI extraction phase plays an important role in palmprint verification, since the accuracy of the whole process is highly dependent on ROI extraction, where several findings have been discussed in previous work [14][15][16][17][18][19][20][21][22][23][24][25]. In [18], ROI extraction steps consist of (i) skin-color thresholding, (ii) hand valley detection, and (iii) finding palm region.…”

Section: Roi Extractionmentioning

confidence: 99%

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Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Izadpanahkakhk

Razavi

Taghipour-Gorjikolaie

et al. 2018

Applied Sciences

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Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier. In order to evaluate our proposed model, we computed the intersection over union (IoU) metric for ROI extraction along with accuracy, receiver operating characteristic (ROC) curves, and equal error rate (EER) for the verification task.The experiments demonstrated that the ROI extraction module could significantly find the appropriate palmprint ROIs, and the verification results were crucially precise. This was verified by different databases and classification methods employed in our proposed model. In comparison with other existing approaches, our model was competitive with the state-of-the-art approaches that rely on the representation of hand-crafted descriptors. We achieved a IoU score of 93% and EER of 0.0125 using a support vector machine (SVM) classifier for the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database. It is notable that all codes are open-source and can be accessed online.

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Section: Roi Extractionmentioning

confidence: 99%

“…To address this problem, statistical segmentation models were employed. In this way, Reference [21] exploited active shape model (ASM), while [22,23] proposed their methods based on the active appearance model (AAM). These approaches suffer from the probability of important features loss due to small extracted ROIs.…”

Section: Roi Extractionmentioning

confidence: 99%

Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Izadpanahkakhk

Razavi

Taghipour-Gorjikolaie

et al. 2018

Applied Sciences

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“…As one of the most powerful model-based algorithms, active appearance model (AAM) (Cootes et al 1998) has been widely used under various situations, for example, deformable object segmentation and tracking (Aykut and Ekinci 2013;Zhong et al 2014), face recognition (Murtaza et al 2013), and expression analysis (Chena et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Face model fitting on video sequences based on incremental virtual active appearance model

Chen

Guo

2015

Multidim Syst Sign Process

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It has been proved that the performance of a person-specific active appearance model (AAM) built to model the appearance variation of a single person across pose, illumination, and expression is substantially better than the performance of a generic AAM built to model the appearance variation of many faces. However, it is not practical to build a personal AAM before tracking an unseen subject. A virtual person-specific AAM is proposed to tackle the problem. The AAM is constructed from a set of virtual personal images with different poses and expressions which are synthesized from the annotated first frame via regressions. To preserve personal facial details on the virtual images, a poison fusion strategy is designed and applied to the virtual facial images generated via bilinear kernel ridge regression. Furthermore, the AAM subspace is sequentially updated during tracking based on sequential Karhunen-Loeve algorithm, which helps the AAM adaptive to the facial context variation. Experiments show the proposed virtual personal AAM is robust to facial context changes during tracking, and outperforms other state-of-the-art AAM on facial feature tracking accuracy and computation cost.

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“…However, ASM relies on the geometry that is sensitive to interference of complex background. Active appearance model (AAM) was proposed in unrestricted posture and background to improve the efficiency, accuracy and robustness [17]. AAM forms a statistical model of shape and texture together, so the computational cost is high.…”

Section: Introductionmentioning

confidence: 99%

Logical Conjunction of Triple-Perpendicular-Directional Translation Residual for Contactless Palmprint Preprocessing

Leng

Liu

et al. 2014

2014 11th International Conference on Information Technology: New Generations

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Contactless palmprint recognition systems alleviate the concerns on personal hygiene, acquisition flexibility, etc. Unfortunately, the preprocessing of contactless palmprint image faces several severe challenges, including unconstrained hand placement, complex background, light interference, etc. This paper proposes logical conjunction of triple-perpendiculardirectional translation residual (TPDTR) for the improvement of contactless palmprint image preprocessing. The search of hand valley point is within the borders of hand valley gap detected by TPDTR; therefore, the computational cost is effectively decreased. Furthermore, the anti-interference capacity of region is stronger than that of point and line, so TPDTR improves the accuracy of hand valley point detection. The experimental results confirm the superiorities of TPDTR over the existing methods in computational cost and accuracy.

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AAM-based palm segmentation in unrestricted backgrounds and various postures for palmprint recognition

Cited by 34 publications

References 20 publications

Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Face model fitting on video sequences based on incremental virtual active appearance model

Logical Conjunction of Triple-Perpendicular-Directional Translation Residual for Contactless Palmprint Preprocessing

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