ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction

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

doi:10.1109/btas.2018.8698580

Cited by 24 publications

(12 citation statements)

References 33 publications

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“…Moreover, the researchers attempted to solve the cross-domain problem by performing domain adaptation using an auto-encoder structured model [16]. Malhotra et al highlighted the need to reinforce the touch-based biometric recognition system as the coronavirus disease (COVID- 19) is increasingly becoming a serious issue across the globe [17]. Accordingly, the system was reinforced so that the fingerprint authentication system implements matching using a finger-selfie image.…”

Section: Training-based Methodsmentioning

confidence: 99%

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Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network

Noh

Choi

Hong

et al. 2021

Sensors

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The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases—Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method.

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Section: Training-based Methodsmentioning

confidence: 99%

“…However, the performance was not satisfactory in the cross-domain environment even when recognition was performed using only compact information. Dabouei et al verified the performance in the cross-sensor environment using a conditional generative adversarial network (CGAN) for fingerprint ridge map reconstruction [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network

Noh

Choi

Hong

et al. 2021

Sensors

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“…On the other hand, some researchers have exploited the potential of using generative adversarial networks (GAN) for latent fingerprint enhancement. Dabouei et al [19] showed that a conditional GAN could reconstruct partial latent fingerprints. Liu et al [20] introduced a cooperative orientation generative adversarial network (COOGAN) to transform latent fingerprint images using a shared representation of ridge enhancement and orientation features.…”

Section: B Deep Learning Approachmentioning

confidence: 99%

Progressive and Corrective Feedback for Latent Fingerprint Enhancement Using Boosted Spectral Filtering and Spectral Autoencoder

2021

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The objective of this research is to design an efficient algorithm that can successfully enhance a targeted latent fingerprint from various complex backgrounds under an uncontrolled environment. Most algorithms in literature exploited dictionary learning schemes and deep learning architectures to capture latent fingerprints from complicated backgrounds and noise. However, an algorithm learned from other high-quality fingerprint images may not solve all possible cases within a given unseen image. We propose a new feedback framework to distinguish latent fingerprints from complex backgrounds and gradually improve friction-ridge quality using the information provided inside a given unseen image. We combine two efficient mechanisms. The first mechanism enhances high-quality areas in priority and feeds the enhanced areas back to improve the quality of latent fingerprints in the nearby area. The second mechanism is to verify that the first mechanism works correctly by detecting anomalously enhanced fingerprint patterns. The second mechanism employs a spectral autoencoder that learns from good fingerprint spectra in the frequency domain. The anomalous fingerprint area is sent back to the first mechanism for further improving the enhanced result. We benchmark the proposed algorithm against available state-of-the-art algorithms using two fingerprint matching systems (one commercial off-the-shelf and one open-source) on two public latent fingerprint databases. The experimental results show that the proposed algorithm outperforms most state-of-the-art algorithms in the literature.

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“…Similarly, as presented in Fig. 5, the fingerprint images are degraded using two corruptions [57], [58]. The first corruption consists of warping the clean fingerprints [57] by randomly sampling the first two principal warp components extracted from the Tsinghua Distorted Fingerprint Database [57], [77].…”

Section: Training Setupmentioning

confidence: 99%

Quality-Aware Multimodal Biometric Recognition

Soleymani¹,

Dabouei²,

Taherkhani³

et al. 2021

Preprint

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We present a quality-aware multimodal recognition framework that combines representations from multiple biometric traits with varying quality and number of samples to achieve increased recognition accuracy by extracting complimentary identification information based on the quality of the samples. We develop a quality-aware framework for fusing representations of input modalities by weighting their importance using quality scores estimated in a weakly-supervised fashion. This framework utilizes two fusion blocks, each represented by a set of quality-aware and aggregation networks. In addition to architecture modifications, we propose two task-specific loss functions: multimodal separability loss and multimodal compactness loss. The first loss assures that the representations of modalities for a class have comparable magnitudes to provide a better quality estimation, while the multimodal representations of different classes are distributed to achieve maximum discrimination in the embedding space. The second loss, which is considered to regularize the network weights, improves the generalization performance by regularizing the framework. We evaluate the performance by considering three multimodal datasets consisting of face, iris, and fingerprint modalities. The efficacy of the framework is demonstrated through comparison with the state-of-the-art algorithms. In particular, our framework outperforms the rankand score-level fusion of modalities of BIOMDATA [1] by more than 30% for true acceptance rate at false acceptance rate of 10 −4 .

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ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction

Cited by 24 publications

References 33 publications

Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network

Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network

Progressive and Corrective Feedback for Latent Fingerprint Enhancement Using Boosted Spectral Filtering and Spectral Autoencoder

Quality-Aware Multimodal Biometric Recognition

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