Fingerprint Distortion Rectification Using Deep Convolutional Neural Networks

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

doi:10.1109/icb2018.2018.00012

Cited by 32 publications

(39 citation statements)

References 35 publications

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“…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]. The other corruption considers fading the fingerprint ridges at random points [58].…”

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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Section: Training Setupmentioning

confidence: 99%

Quality-Aware Multimodal Biometric Recognition

Soleymani¹,

Dabouei²,

Taherkhani³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, deep learning methods have been widely utilized in face recognition and other classification problems [32,26,8,34,35,16,37] instead of classical methods [6,2]. These methods, can also be employed for the task of sketch-photo recognition problem by learning the relationship between the two modalities.…”

Section: Introductionmentioning

confidence: 99%

Deep Sketch-Photo Face Recognition Assisted by Facial Attributes

Iranmanesh

Kazemi

Soleymani

et al. 2018

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

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In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential information about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a coupled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Convolutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common embedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with different facial attributes, while the verification task reduces the intra-personal variations by pulling together all the features that are related to one person. The learned discriminative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary facial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Extensive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the stateof-the-art models in sketch-photo recognition algorithms.

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“…Commer-cial and state-of-the-art methods for recognizing the inked or live-scan fingerprints often fail to process latent samples, even in the preprocessing stage [16]. Therefore, various approaches have been proposed in the literature to tackle the problem of latent [15,34,33,5,1] and distorted [3,26,25] fingerprints.…”

Section: Introductionmentioning

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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Fingerprint Distortion Rectification Using Deep Convolutional Neural Networks

Cited by 32 publications

References 35 publications

Quality-Aware Multimodal Biometric Recognition

Quality-Aware Multimodal Biometric Recognition

Deep Sketch-Photo Face Recognition Assisted by Facial Attributes

ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction

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