Feng Liu

Shenzhen University · College of Computer Science&Software Engineering

Biometric systems are vulnerable to presentation attacks (PAs) performed using various PA instruments (PAIs). Even though there are numerous PA detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. In this work, we empirically prove that the initia...

Fingerprints of Optical Coherence Tomography (OCT) imaging provide 3D volume data which have the nature property of multi-layered tissue structure. This paper, for the first time, attempts to extract minutiae for OCT-based fingerprint by making full use of the merits multi-layered structure of OCT imaging and powerful convolution al neural network...

The robustness and generalization ability of Presentation Attack Detection (PAD) methods is critical to ensure the security of Face Recognition Systems (FRSs). However, in a real scenario, Presentation Attacks (PAs) are various and it is hard to predict the Presentation Attack Instrument (PAI) species that will be used by the attacker. Existing PAD...

The accuracy of 2D face recognition (FR) has progressed significantly due to the availability of large-scale training data. However, the research of deep learning based 3D FR is still in the early stage. Most of available 3D FR generate 2D maps from 3D data and apply existing 2D CNNs to the generated 2D maps for feature extraction. We propose in th...

The technology of optical coherence tomography (OCT) to fingerprint imaging opens up a new research potential for fingerprint recognition owing to its ability to capture depth information of the skin layers. Developing robust and high security Automated Fingerprint Recognition Systems (AFRSs) are possible if the depth information can be fully utili...

By introducing the skip connection to bridge the semantic gap between encoder and decoder, U-shape architecture has been proven to be effective for recovering fine-grained details in dense prediction tasks. However, such a mechanism cannot be directly applied to reconstruction-based anomaly detection, since the skip connection might lead the model...

Deep convolutional neural network (CNN) based models are vulnerable to the adversarial attacks. One of the possible reasons is that the embedding space of CNN based model is sparse, resulting in a large space for the generation of adversarial samples. In this study, we propose a method, denoted as Dynamic Feature Aggregation, to compress the embedd...

Current high-resolution fingerprints are almost entirely represented by sweat pores, resulting in information loss and weak robustness. This is caused by dividing fingerprint representation into pore detection and pore representation. This paper proposes a novel high-resolution fingerprint representation method for recognition based on only one ful...

Due to the diversity of attack materials, fingerprint recognition systems (AFRSs) are vulnerable to malicious attacks. It is thus important to propose effective fingerprint presentation attack detection (PAD) methods for the safety and reliability of AFRSs. However, current PAD methods often exhibit poor robustness under new attack types settings....

Minutiae extraction plays an important role in automated fingerprint identification systems. There are many minutiae extraction methods proposed for various fingerprints (rolled/slap/latent) in the past decades. However, for fingerprints by optical coherence tomography (OCT) imaging which have the essential property of multi-layered tissue structur...

The robustness and generalization ability of Presentation Attack Detection (PAD) methods is critical to ensure the security of Face Recognition Systems (FRSs). However, in the real scenario, Presentation Attacks (PAs) are various and hard to be collected. Existing PAD methods are highly dependent on the limited training set and cannot generalize we...

Due to the diversity of attack materials, fingerprint recognition systems (AFRSs) are vulnerable to malicious attacks. It is of great importance to propose effective Fingerprint Presentation Attack Detection (PAD) methods for the safety and reliability of AFRSs. However, current PAD methods often have poor robustness under new attack materials or s...

Generative Adversarial Networks (GANs) have been widely adopted in various fields. However, existing GANs generally are not able to preserve the manifold of data space, mainly due to the simple representation of discriminator for the real/generated data. To address such open challenges, this paper proposes Manifold-preserved GANs (MaF-GANs), which...

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most dis-criminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgme...

Biometric systems are vulnerable to the Presentation Attacks (PA) performed using various Presentation Attack Instruments (PAIs). Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem. The common pr...

Facial expression recognition (FER) is still a challenging problem if face images are contaminated by occlusions, which lead to not only noisy features but also loss of discriminative features. To address the issue, this paper proposes a novel adversarial disentangled features learning (ADFL) method for recognizing expressions on occluded face imag...

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This article thus proposes a globa...

The vanilla convolutional neural network (CNN) is vulnerable to images with small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features, leading to the lack of feature diversity. In our method , we propose to dynamically...

Automated Fingerprint Recognition Systems (AFRSs) have been threatened by Presentation Attack (PA) since its existence. It is thus desirable to develop effective presentation attack detection (PAD) methods. However, the unpredictable PAs make PAD be a challenging problem. This paper proposes a novel One-Class PAD (OCPAD) method for Optical Coherenc...

As 2D and 3D data present different views of the same face, the features extracted from them can be both complementary and redundant. In this paper, we present a novel and efficient orthogonalization-guided feature fusion network, namely OGF $^2$ Net, to fuse the features extracted from 2D and 3D faces for facial expression recognition. While 2D t...

As a popular living fingerprint feature, sweat pore has been adopted to build robust high resolution automated fingerprint recognition systems (AFRSs). Pore matching is an important step in high resolution fingerprint recognition. This paper proposes a novel pore matching method with high recognition accuracy. The method mainly solves the pore repr...

Human fingers are 3D objects. More information will be provided if three dimensional (3D) fingerprints are available compared with two dimensional (2D) fingerprints. Thus, this chapter firstly collected 3D finger point cloud data by Structured-light Illumination method. Additional features from 3D fingerprint images are then studied and extracted....

Sweat pores on fingerprints have proven to be discriminative features and have recently been successfully employed in automatic fingerprint recognition systems (AFRS). It is crucial to extract pores precisely to achieve high recognition accuracy. In this chapter two extraction methods will be given. The first method is based on a dynamic anisotropi...

This chapter addresses the problem of feature-based 3D reconstruction model for close-range objects. Since it is almost impossible to find pixel-to-pixel correspondences from 2D images by algorithms when the object is imaged on a close range, the selection of feature correspondences, as well as the number and distribution of them, play important ro...

High resolution fingerprint images have been increasingly used in fingerprint recognition. They can provide more fine features (e.g. pores) than standard fingerprint images, which are expected to be helpful for improving the recognition accuracy. It is therefore demanded to investigate whether or not existing quality assessment methods are suitable...

This chapter shows an application of pores in the alignment of high resolution partial fingerprints, which is a crucial step in partial fingerprint recognition. Previously developed fingerprint alignment methods, including minutia-based and non-minutia feature based ones, are unsuitable for partial fingerprints because small fingerprint fragments o...

High-resolution automated fingerprint recognition systems (AFRS) offer higher security because they are able to make use of level 3 features, such as pores, that are not available in lower-resolution (<500 dpi) images. One of the main parameters affecting the quality of a digital fingerprint image and issues such as cost, interoperability, and perf...

This chapter concludes the book. We first highlight again the motivation of compiling such a book on the topic of advanced fingerprint recognition technology, in particular 3D fingerprints and high resolution fingerprints. We then briefly summarize the content of each chapter in the main body of this book. Finally, we discuss the remaining challeng...

Traditional fingerprints are captured in touch-based way, which results in partial or degraded images. Replacement of touch-based fingerprints by touchless ones can promote the development of touchless 3D AFRSs with high security and accuracy. This chapter reviewed technologies involved 3D AFRSs, including 3D fingerprint generation, 3D fingerprint...

Touchless-based fingerprint recognition technology is thought to be an alternative to touch-based systems to solve problems of hygienic, latent fingerprints, and maintenance. However, there are few studies about touchless fingerprint recognition systems due to the lack of a large database and the intrinsic drawback of low ridge-valley contrast of t...

Extended fingerprint features such as pores, dots and incipient ridges have attracted increasing attention from researchers and engineers working on automatic fingerprint recognition systems. A variety of methods have been proposed to combine these features with the traditional minutiae features. This chapter comparatively analyses the parallel and...

Fingerprint matching is an important and essential step in automated fingerprint recognition systems (AFRSs). The noise and distortion of captured fingerprints and the inaccurate of extracted features make fingerprint matching a very difficult problem. With the advent of high resolution fingerprint imaging techniques and the increasing demand for h...

This chapter provides an overview of Part II with focus on the background and development of fingerprint recognition using high resolution images. We first discuss the significance of high resolution fingerprint recognition in the context of fingerprint recognition history, and then introduce fingerprint features, particularly the features availabl...

Traditional fingerprint recognition systems are vulnerable to attacks, such as the use of artificial fingerprints, and poor performance will be achieved if the captured surface fingerprints are of low-quality. Developing high-security and robust fingerprint recognition systems is of increasing concern in modern society. The introduction of optical...

With the development of presentation attacks, Automated Fingerprint Recognition Systems(AFRSs) are vulnerable to presentation attack. Thus, numerous methods of presentation attack detection(PAD) have been proposed to ensure the normal utilization of AFRS. However, the demand of large-scale presentation attack images and the low-level generalization...

Optical Coherence Tomography (OCT) is a high resolution imaging technology probing the internal structure of multilayered tissues. Since it provides subsurface fingerprint information which is identical to the surface texture but unaffected by any surface defects, OCT-based fingerprints open up a new domain for establishing robust and high security...

Fingerprints are among the most widely used biometric modalities and have been successfully applied in various scenarios. For example, in forensics, fingerprints serve as important legal evidence; and in civilian applications, fingerprints are used for access and attendance control as well as other identity services. Thanks to advances in three-dim...

Facial expression recognition (FER) using 2D images has been rapidly developed in the past decade. However, existing 2D-based FER methods seldom consider the impact of identity factors, and do not utilize shape features which have been proven to be effective complement to texture features. Built upon latest 3D face reconstruction methods, this pape...

Optical Coherence Tomography (OCT) is a high resolution imaging technology, which provides a 3D representation of the fingertip skin. This paper for the first time investigates gender classification using those 3D fingerprints. Different with current fingerprint gender classification methods, the raw multiple longitudinal(X-Z) fingertip images of o...

Face recognition using depth data has attracted increasing attention from both academia and industry in the past five years. Previous works show a huge performance gap between high-quality and low-quality depth data. Due to the lack of databases and reasonable evaluations on data quality, very few researchers have focused on boosting depth-based fa...

Traditional commercial automated fingerprint recognition systems (AFRSs) are vulnerable to fake attacks by the use of artificial fingerprints, due to its limitation on resolution and the difficulty of obtaining depth information. Developing new systems with strong anti-spoofing ability are of increasing concern in the current digital age. Inspired...

High-resolution fingerprint recognition has been a hot topic for many years. Compared with a traditional fingerprint image, a high-resolution fingerprint image can provide more features, such as pores and ridge contours. Introducing these features into fingerprint comparison and recognition can improve the recognition accuracy and reduce the risk o...

Sweat pores have been proved to be discriminative and successfully used for automatic fingerprint recognition. It is crucial to extract pores precisely to achieve high recognition accuracy. To extract pores accurately and robustly, we propose a novel coarse-to-fine detection method based on convolutional neural networks (CNN) and logical operation....

This letter proposes an ensemble neural network (Ensem-NN) for skeleton-based action recognition. The Ensem-NN is introduced based on the idea of ensemble learning, “two heads are better than one”. According to the property of skeleton sequences, we design one-dimensional (1D) convolution neural network (CNN) with residual structure as Base-Net. Fr...

Cascaded regression has been recently applied to reconstruct 3D faces from single 2D images directly in shape space, and has achieved state-of-the-art performance. We investigate thoroughly such cascaded regression based 3D face reconstruction approaches from four perspectives that are not well been studied: (1) the impact of the number of 2D landm...

In automatic fingerprint identification systems, it is crucial to segment the fingerprint images. Inspired by the superiority of convolutional neural networks for various classification and regression tasks, we approach fingerprint segmentation as a binary classification problem and propose a convolutional neural network based method for fingerprin...

Fingerprint image quality assessment is a very important task as the performance of automatic fingerprint identification systems relies heavily on the quality of fingerprint images. Existing methods have made many efforts to find out more appropriate solutions, but most of them operate either on full regions of a fingerprint image, or on local area...

The huge number of sweat pores in fingerprint images results in low efficiency of direct pore (DP) matching methods. To overcome this drawback, this paper proposes a feature guided fingerprint pore matching method. It selects “distinctive” pores around the minutiae and singular points from fingerprint images which extremely reduced the number of po...

This paper proposes a new method to extract pores on high resolution fingerprints. The basic idea of this method is to binarize the fingerprint images based on multi-scale morphological transformation, and then extract pores by different strategies. The closed pores are extracted by the size of connected regions, and the open pores are detected usi...

As one of the most important soft biometrics, gender has substantial applications in various areas such as demography and human-computer interaction. Successful gender estimation of face images taken under real-world also contributes to improving the face identification results in the wild. However, most existing gender classification methods estim...

Human fingers are 3D objects. More information will be provided if three dimensional (3D) fingerprints are available compared with two dimensional (2D) fingerprints. Thus, this paper firstly collected 3D finger point cloud data by Structured-light Illumination method. Additional features from 3D fingerprint images are then studied and extracted. Th...

It is of urgency to effectively identify differentially expressed genes from RNA-Seq data. In this paper, we proposed a novel method, joint-L2,1-norm-constraint-based semi-supervised feature extraction (L21SFE), to analyze RNA-Seq data. Our scheme was shown as follows. Firstly, we constructed a graph Laplacian matrix and refined it by using the lab...

Classification based on image sets has recently attracted increasing interests in computer vision and pattern recognition community. It finds numerous applications in real-life scenarios, such as classification from surveillance videos, multi-view camera networks, and personal albums. Image set based face classification highly depends on the consis...

Breast cancer is one of the most common malignant tumors among female. How to effectively discriminate the category of the cancers using the key features/factors is very important in the diagnosis and prediction. In this paper, Jointly Sparse Discriminant Analysis (JSDA) is proposed to explore the key factors in breast cancer and extract the key fe...

Image feature is a significant factor for the success of robust face recognition. Recently sparse representation based classifier (SRC) has been widely applied to robust face recognition by using sparse representation residuals to tolerate disturbed image features (e.g., occluded pixels). In order to deal with more complicated image variations, rob...

This paper addresses the problem of feature-based 3D reconstruction model for close-range objects. Since it is almost impossible to find pixel-to-pixel correspondences from 2D images by algorithms when the object is imaged on a close range, the selection of feature correspondences, as well as the number and distribution of them, play important role...

The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various variations. To achieve much stronger robustness to facial occlusion, recently regularized robust coding (RRC) was proposed by designing a new robust representation residual term. Although RRC has achieved the leading performan...

Touchless-based fingerprint recognition technology is thought to be an alternative to touch-based systems to solve problems of hygienic, latent fingerprints, and maintenance. However, there are few studies about touchless fingerprint recognition systems due to the lack of a large database and the intrinsic drawback of low ridge-valley contrast of t...

Touchless fingerprint capture devices have the advantage over traditional touch-based approaches of being hygienic and preventing distortions resulting from the contact of fingers. Single-view acquisition systems bring in problems, such as scene difference and a limited effective area. This paper thus presents a touchless multiview fingerprint capt...

Fingerprint matching is an important and essential step in automated fingerprint recognition systems (AFRSs). The noise and distortion of captured fingerprints and the inaccurate of extracted features make fingerprint matching a very difficult problem. With the advent of high-resolution fingerprint imaging techniques and the increasing demand for h...

High resolution fingerprint images have been increasingly used in fingerprint recognition. They can provide more fine features (e.g. pores) than standard fingerprint images to improve the recognition accuracy. It is however still an open issue whether or not existing quality assessment methods are suitable for high resolution fingerprint images. Th...

Extended fingerprint features such as pores, dots and incipient ridges have been increasingly attracting attention from researchers and engineers working on automatic fingerprint recognition systems. A variety of methods have been proposed to combine these features with the traditional minutiae features. This paper comparatively analyses the parall...

In this paper, we propose a watermarking algorithm for digital image based on DCT and SVD. The algorithm can satisfy the transparence and robustness of the watermarking system very well. The experiment based on this algorithm demonstrates that the watermarking is robust to the common signal processing techniques including JEPG compressing,noise, lo...