Cross-Spectral Periocular Recognition by Cascaded Spectral Image Transformation

Raja, Kiran B.; Damer, Naser; Ramachandra, Raghavendra; Busch, Christoph

doi:10.1109/ist48021.2019.9010520

Cited by 11 publications

(4 citation statements)

References 24 publications

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“…30 explored conditional generative adversarial networks (cGANs) to convert periocular images from one domain to another for verification purposes. Raja et al 31 . proposed a cascaded refinement network to synthesize periocular images from one domain to another.…”

Section: Related Workmentioning

confidence: 99%

“…30 explored conditional generative adversarial networks (cGANs) to convert periocular images from one domain to another for verification purposes. Raja et al 31 proposed a cascaded refinement network to synthesize periocular images from one domain to another. Both handcrafted and deep features were extracted from the synthesized images, and verification experiments were performed.…”

Section: Introductionmentioning

confidence: 99%

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Dual-spectrum network: exploring deep visual feature to attribute mapping for cross-spectral periocular recognition

Behera

Puhan

2023

J. Electron. Imag.

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.Recognition of individuals using periocular images has become popular due to its ease of use in situations in which it is difficult to capture full-face regions or high-resolution iris images. Practical surveillance systems recognize individuals from images acquired in various imaging conditions. Recognition of individuals in cross-spectral environments is of practical interest as it can be used to match probe and gallery images that are captured in distinct wavelength domains. The process of matching probe near-infrared images with a gallery of visible images is significantly challenging due to variations in appearance and illumination conditions. In periocular regions, alleviating this wide appearance gap from such local regions is highly challenging. In this work, we propose a dual-spectrum network that considers both high-level deep features and low-level handcrafted attributes to match periocular images in cross-spectral scenarios. A mapping network is designed to establish the relationship between both types of features and generate mapped features that carry discriminative information relevant to both feature domains. The mapped features are passed through a distance layer and subsequent classification block to determine whether they are a genuine pair or an imposter pair. The network is evaluated on a publicly available PolyU dataset as well as two newly created in-house CASIA and IITBBS cross-spectral periocular datasets. The IITBBS dataset consists of visible and near-infrared periocular images from 100 subjects captured in unconstrained imaging conditions with pose and accessory variations. Extensive experiments indicate that the proposed network achieves verification accuracies of 97.96%, 96.48%, and 97.89% on the PolyU, CASIA, and IITBBS datasets, respectively. Performance analysis with existing methods confirms that our network achieves comparable results on the PolyU and CASIA datasets. In addition, the network outperforms existing works on the challenging IITBBS dataset by achieving improvements of 0.43% and 0.16% in terms of verification accuracy and genuine acceptance rate at a 0.1 false acceptance rate.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dual-spectrum network: exploring deep visual feature to attribute mapping for cross-spectral periocular recognition

Behera

Puhan

2023

J. Electron. Imag.

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“…We have also noticed similar works in the ocular biometric field for the task of cross-spectral periocular image recognition. Recently, Reja et al [48] proposed a novel image transformation technique using cascaded refinement networks to synthesize a NIR periocular image from the corresponding VIS periocular image. Another study [49] reported that feature-based approaches are prone to changes during the feature extraction process.…”

Section: Related Workmentioning

confidence: 99%

Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition

Mostofa

Dawson

Nasrabadi

2021

IEEE Trans. Biom. Behav. Identity Sci.

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In recent years, cross-spectral iris recognition has emerged as a promising biometric approach to establish the identity of individuals. However, matching iris images acquired at different spectral bands (i.e., matching a visible (VIS) iris probe to a gallery of near-infrared (NIR) iris images or vice versa) shows a significant performance degradation when compared to intraband NIR matching. Hence, in this paper, we have investigated a range of deep convolutional generative adversarial network (DCGAN) architectures to further improve the accuracy of cross-spectral iris recognition methods. Moreover, unlike the existing works in the literature, we introduce a resolution difference into the classical cross-spectral matching problem domain. We have developed two different novel techniques using the conditional generative adversarial network (cGAN) as a backbone architecture for cross-spectral iris matching. In the first approach, we simultaneously address the cross-resolution and cross-spectral matching problem by training a cGAN that jointly translates cross-resolution as well as cross-spectral tasks to the same resolution and within the same spectrum. In the second approach, we design a coupled generative adversarial network (cpGAN) architecture consisting of a pair of cGAN modules that project the VIS and NIR iris images into a low-dimensional embedding domain to ensure maximum pairwise similarity between the feature vectors from the two iris modalities of the same subject. To assure the efficacy of our methods, we perform several experiments considering multiple real-life scenarios on three publicly-available cross-spectral iris datasets. Our best experimental results obtained from the cpGAN network outperform the existing benchmark convolutional neural network (CNN) with a supervised discrete hashing (SDH) approach [1] by as much as 1.67%, and 2.22% GAR at FAR of 0.01, while our cGAN provides recognition accuracy with significantly lower EER value of 1.5%, and 1.54% for PolyU bi-spectral dataset, and Cross-eyed-cross-spectral iris recognition database, respectively. It indicates the superiority of our approaches over results previously published in the literature.

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“…It also carries other benefits; for example, perspective distortion affects the periocular region to a lower degree because the depth variation of the area is smaller than the that of the complete face [18]. Periocular recognition on smartphones and wearable devices has gained growing research attention, and with recent deep learning methods achieving superior accuracy and with applications in various domains have surfaced [19][20][21][22]. However, deploying large models based on deep learning on resource-critical consumer devices presents two main challenges.…”

Section: Introductionmentioning

confidence: 99%

Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models

Damer

Raja

Kirchbuchner

et al. 2022

Sensors

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This work addresses the challenge of building an accurate and generalizable periocular recognition model with a small number of learnable parameters. Deeper (larger) models are typically more capable of learning complex information. For this reason, knowledge distillation (kd) was previously proposed to carry this knowledge from a large model (teacher) into a small model (student). Conventional KD optimizes the student output to be similar to the teacher output (commonly classification output). In biometrics, comparison (verification) and storage operations are conducted on biometric templates, extracted from pre-classification layers. In this work, we propose a novel template-driven KD approach that optimizes the distillation process so that the student model learns to produce templates similar to those produced by the teacher model. We demonstrate our approach on intra- and cross-device periocular verification. Our results demonstrate the superiority of our proposed approach over a network trained without KD and networks trained with conventional (vanilla) KD. For example, the targeted small model achieved an equal error rate (EER) value of 22.2% on cross-device verification without KD. The same model achieved an EER of 21.9% with the conventional KD, and only 14.7% EER when using our proposed template-driven KD.

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Cross-Spectral Periocular Recognition by Cascaded Spectral Image Transformation

Cited by 11 publications

References 24 publications

Dual-spectrum network: exploring deep visual feature to attribute mapping for cross-spectral periocular recognition

Dual-spectrum network: exploring deep visual feature to attribute mapping for cross-spectral periocular recognition

Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition

Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models

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