Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification

Soleymani, Sobhan; Dabouei, Ali; Kazemi, Hadi; Dawson, Jeremy; Nasrabadi, Nasser M.

doi:10.1109/icpr.2018.8545061

Cited by 81 publications

(63 citation statements)

References 39 publications

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“…The relevant works that deal with non-ideal ocular are those by Zhang et al [26] and Soleymani et al [27]. Zhang et al [26] fused iris and periocular modalities through a weighted concatenation.…”

Section: Related Workmentioning

confidence: 99%

Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN

2019

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Periocular recognition remains challenging for deployments in the unconstrained environments. Therefore, this paper proposes an RGB-OCLBCP dual-stream convolutional neural network, which accepts an RGB ocular image and a colour-based texture descriptor, namely Orthogonal Combination-Local Binary Coded Pattern (OCLBCP) for periocular recognition in the wild. The proposed network aggregates the RGB image and the OCLBCP descriptor by using two distinct late-fusion layers. We demonstrate that the proposed network benefits from the RGB image and thee OCLBCP descriptor can gain better recognition performance. A new database, namely an Ethnic-ocular database of periocular in the wild, is introduced and shared for benchmarking. In addition, three publicly accessible databases, namely AR, CASIA-iris distance and UBIPr, have been used to evaluate the proposed network. When compared against several competing networks on these databases, the proposed network achieved better performances in both recognition and verification tasks.

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Section: Related Workmentioning

confidence: 99%

Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN

2019

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“…Recently, deep learning models have outperformed the classical machine learning models in a variety of application areas, including biometrics [26,27,25]. However, these models are vulnerable to a small perturbation in the input image.…”

Section: Adversarial Attacksmentioning

confidence: 99%

Adversarial Examples to Fool Iris Recognition Systems

Soleymani

Dabouei

Dawson

et al. 2019

2019 International Conference on Biometrics (ICB)

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Adversarial examples have recently proven to be able to fool deep learning methods by adding carefully crafted small perturbation to the input space image. In this paper, we study the possibility of generating adversarial examples for code-based iris recognition systems. Since generating adversarial examples requires back-propagation of the adversarial loss, conventional filter bank-based iris-code generation frameworks cannot be employed in such a setup. Therefore, to compensate for this shortcoming, we propose to train a deep auto-encoder surrogate network to mimic the conventional iris code generation procedure. This trained surrogate network is then deployed to generate the adversarial examples using the iterative gradient sign method algorithm [15]. We consider non-targeted and targeted attacks through three attack scenarios. Considering these attacks, we study the possibility of fooling an iris recognition system in white-box and black-box frameworks.

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“…Second, the generator should preserve the identification information embedded in minutiae and ridge patterns. To extract and preserve the identification information we developed a method that is inspired by perceptual loss [4,7,12,17] and multi-level feature abstraction [27,28,13,10]. For this purpose, we separately trained a deep Siamese CNN as a fingerprint verifier.…”

Section: Cgan For Latent Fingerprint Reconstructionmentioning

confidence: 99%

ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction

Dabouei

Soleymani

Kazemi

et al. 2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

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Performing recognition tasks using latent fingerprint samples is often challenging for automated identification systems due to poor quality, distortion, and partially missing information from the input samples. We propose a direct latent fingerprint reconstruction model based on conditional generative adversarial networks (cGANs). Two modifications are applied to the cGAN to adapt it for the task of latent fingerprint reconstruction. First, the model is forced to generate three additional maps to the ridge map to ensure that the orientation and frequency information are considered in the generation process, and prevent the model from filling large missing areas and generating erroneous minutiae. Second, a perceptual ID preservation approach is developed to force the generator to preserve the ID information during the reconstruction process. Using a synthetically generated database of latent fingerprints, the deep network learns to predict missing information from the input latent samples. We evaluate the proposed method in combination with two different fingerprint matching algorithms on several publicly available latent fingerprint datasets. We achieved rank-10 accuracy of 88.02% on the IIIT-Delhi latent fingerprint database for the task of latent-to-latent matching and rank-50 accuracy of 70.89% on the IIIT-Delhi MOLF database for the task of latent-to-sensor matching. Experimental results of matching reconstructed samples in both latent-to-sensor and latent-to-latent frameworks indicate that the proposed method significantly increases the matching accuracy of the fingerprint recognition systems for the latent samples.

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Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification

Cited by 81 publications

References 39 publications

Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN

Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN

Adversarial Examples to Fool Iris Recognition Systems

ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction

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