Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Fingerprint Recognition <s> This work presents local discriminative feature learning for fingerprint liveness detection.Our method does not require specific knowledge regarding live or fake fingerprints.Our method does not require specific knowledge on recognition systems.Our method can be performed with relatively low computation complexity.Our method achieves good accuracy on various sensor datasets of the LivDet2013 test. Fingerprint recognition systems are vulnerable to impersonation by fake or spoof fingerprints. Fingerprint liveness detection is a step to ensure whether a scanned fingerprint is live or fake prior to a recognition step. This paper presents a fingerprint liveness detection method based on a deep belief network (DBN). A DBN with multiple layers of restricted Boltzmann machine is used to learn features from a set of live and fake fingerprints and also to detect the liveness. The proposed method is a systematic application of a deep learning technique, and does not require specific domain expertise regarding fake fingerprints or recognition systems. The proposed method provides accurate detection of the liveness with various sensor datasets collected for the international fingerprint liveness detection competition. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Fingerprint Recognition <s> With the growing use of biometric authentication systems in the recent years, spoof fingerprint detection has become increasingly important. In this paper, we use convolutional neural networks (CNNs) for fingerprint liveness detection. Our system is evaluated on the data sets used in the liveness detection competition of the years 2009, 2011, and 2013, which comprises almost 50 000 real and fake fingerprints images. We compare four different models: two CNNs pretrained on natural images and fine-tuned with the fingerprint images, CNN with random weights, and a classical local binary pattern approach. We show that pretrained CNNs can yield the state-of-the-art results with no need for architecture or hyperparameter selection. Data set augmentation is used to increase the classifiers performance, not only for deep architectures but also for shallow ones. We also report good accuracy on very small training sets (400 samples) using these large pretrained networks. Our best model achieves an overall rate of 97.1% of correctly classified samples—a relative improvement of 16% in test error when compared with the best previously published results. This model won the first prize in the fingerprint liveness detection competition 2015 with an overall accuracy of 95.5%. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Fingerprint Recognition <s> The high variability of fingerprint data (owing to, e.g., differences in quality, moisture conditions, and scanners) makes the task of minutiae extraction challenging, particularly when approached from a stance that relies on tunable algorithmic components, such as image enhancement. We pose minutiae extraction as a machine learning problem and propose a deep neural network — MENet, for Minutiae Extraction Network — to learn a data-driven representation of minutiae points. By using the existing capabilities of several minutiae extraction algorithms, we establish a voting scheme to construct training data, and so train MENet in an automated fashion on a large dataset for robustness and portability, thus eliminating the need for tedious manual data labelling. We present a post-processing procedure that determines precise minutiae locations from the output of MENet. We show that MENet performs favourably in comparisons against existing minutiae extractors. <s> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Fingerprint Recognition <s> Minutiae extraction is of critical importance in automated fingerprint recognition. Previous works on rolled/slap fingerprints failed on latent fingerprints due to noisy ridge patterns and complex background noises. In this paper, we propose a new way to design deep convolutional network combining domain knowledge and the representation ability of deep learning. In terms of orientation estimation, segmentation, enhancement and minutiae extraction, several typical traditional methods performed well on rolled/slap fingerprints are transformed into convolutional manners and integrated as an unified plain network. We demonstrate that this pipeline is equivalent to a shallow network with fixed weights. The network is then expanded to enhance its representation ability and the weights are released to learn complex background variance from data, while preserving end-to-end differentiability. Experimental results on NIST SD27 latent database and FVC 2004 slap database demonstrate that the proposed algorithm outperforms the state-of-the-art minutiae extraction algorithms. Code is made publicly available at: this https URL <s> BIB004 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Fingerprint Recognition <s> Contactless 2D fingerprint identification is more hygienic, and enables deformation free imaging for higher accuracy. Success of such emerging contactless fingerprint technologies requires advanced capabilities to accurately match such fingerprint images with the conventional fingerprint databases which have been developed and deployed in last two decades. Convolutional neural networks have shown remarkable success for the face recognition problem. However, there has been very few attempts to develop CNN-based methods to address challenges in fingerprint identification problems. This paper proposes a multi-Siamese CNN architecture for accurately matching contactless and contact-based fingerprint images. In addition to the fingerprint images, hand-crafted fingerprint features, e.g. minutiae and core point, are also incorporated into the proposed architecture. This multi-Siamese CNN is trained using the fingerprint images and extracted features. Therefore, a more robust deep fingerprint representation is formed from the concatenation of deep feature vectors generated from multi-networks. In order to demonstrate the effectiveness of the proposed approach, a publicly available database consisting of contact-based and respective contactless finger-prints is utilized. The experimental evaluations presented in this paper achieve outperforming results, over other CNN-based methods and the traditional fingerprint cross matching methods, and validate our approach. <s> BIB005 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Fingerprint Recognition <s> Latent fingerprints are important evidences used by law enforcement agencies to identify suspects for centuries. However, due to the poor image quality and complex background noise, separating the fingerprint region-of-interest from complex background is a very challenging problem. This paper proposes a new latent fingerprint segmentation method based on Convolutional Neural Networks (ConvNets). The latent fingerprint segmentation problem is formulated as a classification system, in which a set of elaborately designed ConvNets is learned to classify each patch as either fingerprint or background. Considering the spatial correlation between fingerprint patches, we proposed to train the set of ConveNets using multi-sized overlapping patches to utilize complementary information. Then, a score map is calculated based on the classification results to evaluate the possibility of a pixel belonging to the fingerprint foreground. Finally, a segmentation mask is generated by thresholding the score map and used to delineate the latent fingerprint boundary. Experimental results on NIST SD27 latent database demonstrate that the proposed method outperforms the existing benchmarks in terms of both false detection rate (FDR) and overall segmentation accuracy. Thanks to the off-line training and short segmentation running time, the proposed method is applicable to applications such as latent fingerprint matching. <s> BIB006 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Fingerprint Recognition <s> Abstract Contactless 3D fingerprint identification has gained significant attentions in recent years as it can offer more hygienic, accurate and ubiquitous personal identification. Despite such advantages, contactless 3D imaging often results in partial 3D fingerprints as it requires relatively higher cooperation from users during the contactless 3D imaging. Such contactless 3D fingerprint images significantly degrade matching accuracy due to partial 3D fingerprint imaging. This paper proposes an end-to-end contactless 3D fingerprint representation learning model based on convolutional neural network (CNN). The proposed model includes one fully convolutional network for fingerprint segmentation and three Siamese networks to learn multi-view 3D fingerprint feature representation. Contactless partial 3D fingerprint identification is a more challenging problem due to its high degree of freedom during contactless 3D fingerprint acquisition and is also addressed by using proposed model. We therefore investigate multi-view 3D fingerprint recognition and partial 3D fingerprint using proposed approach. Comparative experimental results, presented in this paper using state-of-the-art 3D fingerprint recognition method, demonstrate the effectiveness of the proposed multi-view approach and illustrate a significant improvement of state-of-the-art 3D fingerprint recognition methods. <s> BIB007 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Fingerprint Recognition <s> Generating realistic biometric images has been an interesting and, at the same time, challenging problem. Classical statistical models fail to generate realistic-looking fingerprint images, as they are not powerful enough to capture the complicated texture representation in fingerprint images. In this work, we present a machine learning framework based on generative adversarial networks (GAN), which is able to generate fingerprint images sampled from a prior distribution (learned from a set of training images). We also add a suitable regularization term to the loss function, to impose the connectivity of generated fingerprint images. This is highly desirable for fingerprints, as the lines in each finger are usually connected. We apply this framework to two popular fingerprint databases, and generate images which look very realistic, and similar to the samples in those databases. Through experimental results, we show that the generated fingerprint images have a good diversity, and are able to capture different parts of the prior distribution. We also evaluate the Frechet Inception distance (FID) of our proposed model, and show that our model is able to achieve good quantitative performance in terms of this score. <s> BIB008 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Fingerprint Recognition <s> Fingerprint alteration, also referred to as obfuscation presentation attack, is to intentionally tamper or damage the real friction ridge patterns to avoid identification by an AFIS. This paper proposes a method for detection and localization of fingerprint alterations. Our main contributions are: (i) design and train CNN models on fingerprint images and minutiae-centered local patches in the image to detect and localize regions of fingerprint alterations, and (ii) train a Generative Adversarial Network (GAN) to synthesize altered fingerprints whose characteristics are similar to true altered fingerprints. A successfully trained GAN can alleviate the limited availability of altered fingerprint images for research. A database of 4,815 altered fingerprints from 270 subjects, and an equal number of rolled fingerprint images are used to train and test our models. The proposed approach achieves a True Detection Rate (TDR) of 99.24% at a False Detection Rate (FDR) of 2%, outperforming published results. The synthetically generated altered fingerprint dataset will be open-sourced. <s> BIB009 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Fingerprint Recognition <s> Finger texture (FT) is currently attracting significant attention in the area of human recognition. FT covers the area between the lower knuckle of the finger and the upper phalanx before the fingerprint. It involves rich features which can be efficiently used as a biometric characteristic. In this study, the authors contribute to this growing area by proposing a new verification approach, i.e. deep FT learning. To the best of the authors' knowledge, this is the first time that deep learning is employed for recognising people by using the FT characteristic. Four databases have been used to evaluate the proposed method: the Hong Kong Polytechnic University Contact-free 3D/2D (PolyU2D), Indian Institute of Technology Delhi (IITD), CASIA Blue spectral (CASIA-BLU) corresponding to spectral 460 nm and CASIA White spectral (CASIA-WHT) from the CASIA Multi-Spectral images database. The obtained results have shown superior performance compared with recent literature. The verification accuracies have attained 100, 98.65, 100 and 98% for the four databases of PolyU2D, IITD, CASIA-BLU and CASIA-WHT, respectively. <s> BIB010 </s> Biometric Recognition Using Deep Learning: A Survey <s> Deep Learning Works on Fingerprint Recognition <s> Fingerprint recognition has been utilized for cellphone authentication, airport security and beyond. Many different features and algorithms have been proposed to improve fingerprint recognition. In this paper, we propose an end-to-end deep learning framework for fingerprint recognition using convolutional neural networks (CNNs) which can jointly learn the feature representation and perform recognition. We train our model on a large-scale fingerprint recognition dataset, and improve over previous approaches in terms of accuracy. Our proposed model is able to achieve a very high recognition accuracy on a well-known fingerprint dataset. We believe this framework can be widely used for biometrics recognition tasks, making more scalable and accurate systems possible. We have also used a visualization technique to highlight the important areas in an input fingerprint image, that mostly impact the recognition results. <s> BIB011
There have been numerous works on using deep learning for fingerprint recognition. Here we provide a summary of some of the prominent works in this area. In BIB003 , Darlow et al. proposed a fingerprint minutiae extraction algorithm based on deep learning models, called MENet, and achieve promising results on fingerprint images from FVC datasets. In BIB004 , Tang and colleagues proposed another deep learning-based model for fingerprint minutiae extraction, called Fin-gerNet. This model jointly performs feature extraction, orientation estimation, segmentation, and uses them to estimate the minutiae maps. The block-diagram of this model is shown in Figure 13 . . In another work BIB007 , Lin and Kumar proposed a multi-view deep representation (based on CNNs) for contact-less and partial 3D fingerprint recognition. The proposed model includes one fully convolutional network for fingerprint segmentation and three Siamese networks to learn multi-view 3D fingerprint feature representation. They show promising results on several 3D fingerprint databases. In BIB010 , the authors develop a fingerprint texture learning using a deep learning framework. They evaluate their models on several benchmarks, and achieve verification accuracies of 100, 98.65, 100 and 98% on the four databases of PolyU2D, IITD, CASIA-BLU and CASIA-WHT, respectively. In BIB011 , Minaee et al. proposed a deep transfer learning approach to perform fingerprint recognition with a very high accuracy. They fine-tuned a pre-trained ResNet model on a popular fingerprint dataset, and are able to achieve very high recognition rate. In BIB005 , Lin and Kumar proposed a multi-Siamese network to accurately match contactless to contactbased fingerprint images. In addition to the fingerprint images, hand-crafted fingerprint features, e.g., minutiae and core point, are also incorporated into the proposed architecture. This multi-Siamese CNN is trained using the fingerprint images and extracted features. There are also some works using deep learning models for fingerprint segmentation. In  , Stojanovic and colleagues proposed a fingerprint ROI segmentation algorithm based on convolutional neural networks. In another work BIB006 , Zhu et al. proposed a new latent fingerprint segmentation method based on convolutional neural networks ("ConvNets"). The latent fingerprint segmentation problem is formulated as a classification system, in which a set of ConvNets are trained to classify each patch as either fingerprint or background. Then, a score map is calculated based on the classification results to evaluate the possibility of a pixel belonging to the fingerprint foreground. Finally, a segmentation mask is generated by thresholding the score map and used to delineate the latent fingerprint boundary. There have also been some works for fake fingerprint detection. In BIB001 , Kim et al. proposed a fingerprint liveliness detection based on statistical features learned from deep belief network (DBN). This method achieves good accuracy on various sensor datasets of the LivDet2013 test. In BIB002 , Nogueira and colleagues proposed a model to detect fingerprint liveliness (where they are real or fake) using a convolutional neural network, which achieved an accuracy of 95.5% on fingerprint liveness detection competition 2015. There have also been some works on using generative models for fingerprint image generation. In BIB008 , Minaee et al proposed an algorithm for fingerprint image generation based on an extension of GAN, called "Connectivity Imposed GAN". This model adds total variation of the generated image to the GAN loss function, to promote the connectivity of generated fingerprint images. In BIB009 , Tabassi et al. developed a framework to synthesize altered fingerprints whose characteristics are similar to true altered fingerprints, and used them to train a classifier to detect "Fingerprint alteration/obfuscation presentation attack" (i.e. intentional Fig. 13 The block-diagram of the proposed FingerNet model for minutiae extraction. Courtesy of BIB004 . tamper or damage to the real friction ridge patterns to avoid identification).