当前期刊: IET Biometrics Go to current issue    加入关注   
显示样式:        排序: IF: - GO 导出
  • Deep convolutional neural networks for face and iris presentation attack detection: survey and case study
    IET Biom. (IF 1.821) Pub Date : 2020-08-25
    Yomna Safaa El-Din; Mohamed N. Moustafa; Hani Mahdi

    Biometric presentation attack detection (PAD) is gaining increasing attention. Users of mobile devices find it more convenient to unlock their smart applications with finger, face, or iris recognition instead of passwords. In this study, the authors survey the approaches presented in the recent literature to detect face and iris presentation attacks. Specifically, they investigate the effectiveness

  • Fingerprint enhancement using multi-scale classification dictionaries with reduced dimensionality
    IET Biom. (IF 1.821) Pub Date : 2020-08-25
    Deqin Xu; Weixin Bian; Yongqiang Cheng; Qingde Li; Yonglong Luo; Qingying Yu

    In order to improve the quality of fingerprint with a large noise, this study proposes a fingerprint enhancement method by using a sparse representation of learned multi-scale classification dictionaries with reduced dimensionality. The multi-scale dictionary is used to balance the contradiction between the accuracy and the anti-noise ability, which is an ideal solution to reconcile the demands of

  • Two-tiered face verification with low-memory footprint for mobile devices
    IET Biom. (IF 1.821) Pub Date : 2020-08-25
    Rafael Padilha; Fernanda A. Andaló; Gabriel Bertocco; Waldir R. Almeida; William Dias; Thiago Resek; Ricardo da S. Torres; Jacques Wainer; Anderson Rocha

    Mobile devices have their popularity and affordability greatly increased in recent years. As a consequence of their ubiquity, these devices now carry all sorts of personal data that should be accessed only by their owner. Even though knowledge-based procedures are still the main methods to secure the owner's identity, recently biometric traits have been employed for more secure and effortless authentication

  • Constant-Q magnitude–phase coefficients extraction for synthetic speech detection
    IET Biom. (IF 1.821) Pub Date : 2020-08-25
    Jichen Yang; Pei Lin; Qianhua He

    Previous works in synthetic speech detection have focused on features based on magnitude or phase spectrum. In this study, to extract useful discriminative information for synthetic speech detection, the authors propose a feature based on magnitude–phase spectrum (MPS), combining magnitude- and phase-spectra information. The proposed feature is termed as constant-Q magnitude–phase coefficient (CMPC)

  • Discriminative common feature subspace learning for age-invariant face recognition
    IET Biom. (IF 1.821) Pub Date : 2020-06-10
    Yu-Feng Yu; Qiangchang Wang; Min Jiang

    Considering human ageing has a big impact on cross-age face recognition, and the effect of ageing on face recognition in non-ideal images has not been well addressed yet. In this study, the authors propose a discriminative common feature subspace learning method to deal with the problem. Specifically, they consider the samples of the same individual with big age gaps have different distributions in

  • Palmprint recognition using state-of-the-art local texture descriptors: a comparative study
    IET Biom. (IF 1.821) Pub Date : 2020-06-10
    Abdellatif El Idrissi; Youssef El merabet; Yassine Ruichek

    Several human being traits can be used as a robust and distinctive identifier for a given person. The palm region of the hand is one of these features that researchers in biometric fields have given a huge consideration in recent years. Many works have been proposed in the literature to design palmprint (an image acquired of the palm region) recognition framework. Extraction of prominent image local

  • PRNU-based detection of facial retouching
    IET Biom. (IF 1.821) Pub Date : 2020-06-10
    Christian Rathgeb; Angelika Botaljov; Fabian Stockhardt; Sergey Isadskiy; Luca Debiasi; Andreas Uhl; Christoph Busch

    Nowadays, many facial images are acquired using smart phones. To ensure the best outcome, users frequently retouch these images before sharing them, e.g. via social media. Modifications resulting from used retouching algorithms might be a challenge for face recognition technologies. Towards deploying robust face recognition as well as enforcing anti-photoshop legislations, a reliable detection of retouched

  • BMIAE: blockchain-based multi-instance Iris authentication using additive ElGamal homomorphic encryption
    IET Biom. (IF 1.821) Pub Date : 2020-06-10
    Morampudi Mahesh Kumar; Munaga V. N. K. Prasad; U.S.N. Raju

    Multi-biometric systems have been widely accepted in various applications due to its capability to solve the limitations of unimodal systems. Directly storing the biometric templates into a centralised server leads to privacy concerns. In the past few years, many biometric authentication systems based on homomorphic encryption have been introduced to provide security for the templates. Most of the

  • Weighted quasi-arithmetic mean based score level fusion for multi-biometric systems
    IET Biom. (IF 1.821) Pub Date : 2020-04-30
    Herbadji Abderrahmane; Guermat Noubeil; Ziet Lahcene; Zahid Akhtar; Dipankar Dasgupta

    Biometrics is now being principally employed in many daily applications ranging from the border crossing to mobile user authentication. In the high-security scenarios, biometrics require stringent accuracy and performance criteria. Towards this aim, multi-biometric systems that fuse the evidences from multiple sources of biometric have exhibited to diminish the error rates and alleviate inherent frailties

  • 3D face mask presentation attack detection based on intrinsic image analysis
    IET Biom. (IF 1.821) Pub Date : 2020-04-30
    Lei Li; Zhaoqiang Xia; Xiaoyue Jiang; Yupeng Ma; Fabio Roli; Xiaoyi Feng

    Face presentation attacks have become a major threat against face recognition systems and many countermeasures have been proposed over the past decade. However, most of them are devoted to 2D face presentation attack detection, rather than 3D face masks. Unlike the real face, the 3D face mask is usually made of resin materials and has a smooth surface, resulting in reflectance differences. Therefore

  • Deep learning for face recognition on mobile devices
    IET Biom. (IF 1.821) Pub Date : 2020-04-30
    Belén Ríos-Sánchez; David Costa-da Silva; Natalia Martín-Yuste; Carmen Sánchez-Ávila

    Mobility implies a great variability of capturing conditions, which is not easy to control and directly affects to face detection and the extraction of facial features. Deep learning solutions seem to be the most interesting choice for automatic face recognition, but they are highly dependent on the model generated during the training stage. In addition, the size of the models makes it difficult for

  • Cost-effective and accurate palm vein recognition system based on multiframe super-resolution algorithms
    IET Biom. (IF 1.821) Pub Date : 2020-04-30
    Venance Kilian; Nassor Ally; Josiah Nombo; Abdi T. Abdalla; Baraka Maiseli

    Palm vein recognition (PVR) refers to the contactless biometric identification method that uses palm vein patterns to confirm the identity of a person. Compared with other methods, PVR has received a wide attention because it provides more secure results. The veins, being located inside the human body, make PVR robust against tempering and changes in morphology of body features. Most PVR systems integrate

  • Online writer identification system using adaptive sparse representation framework
    IET Biom. (IF 1.821) Pub Date : 2020-04-30
    Vivek Venugopal; Suresh Sundaram

    This study explores an adaptive sparse representation approach for online writer identification. The main focus is on employing prior information that quantifies the degree of importance of a dictionary atom concerning a given writer. This information is proposed by a fusion of two derived components. The first component is a saliency measure obtained from the sum-pooled sparse coefficients corresponding

  • Universal fingerprint minutiae extractor using convolutional neural networks
    IET Biom. (IF 1.821) Pub Date : 2020-02-20
    Van Huan Nguyen; Jinsong Liu; Thi Hai Binh Nguyen; Hakil Kim

    Minutiae, widely used feature points of fingerprint images, directly decide the performance of fingerprint recognition. Conventional minutiae extractors rely on a series of preprocessing steps, thus performing poorly for bad quality samples due to error accumulations. Existing extractors using convolutional neural networks are trained and tested with a certain specific sensor, thus requiring various

  • Face recognition: a novel multi-level taxonomy based survey
    IET Biom. (IF 1.821) Pub Date : 2020-02-20
    Alireza Sepas-Moghaddam; Fernando M. Pereira; Paulo Lobato Correia

    In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment. To help to understand the landscape and abstraction levels relevant for face recognition systems, face recognition taxonomies allow a deeper dissection and comparison

  • Deep representations for cross-spectral ocular biometrics
    IET Biom. (IF 1.821) Pub Date : 2020-02-20
    Luiz A. Zanlorensi; Diego Rafael Lucio; Alceu de Souza Britto Junior; Hugo Proença; David Menotti

    One of the major challenges in ocular biometrics is the cross-spectral scenario, i.e. how to match images acquired in different wavelengths. This study designs and extensively evaluates cross-spectral ocular verification methods using well known deep learning representations based on the iris and periocular regions. Using as inputs, the bounding boxes of non-normalised iris-periocular regions, the

  • Efficient method for segmentation of noisy and non-circular iris images using improved particle swarm optimisation-based MRFCM
    IET Biom. (IF 1.821) Pub Date : 2020-02-20
    Rapaka Satish; P. Rajesh Kumar

    Segmentation of the iris is a crucial stage in an automated iris-based recognition system. The performance of any biometric system primarily relies on how effectively the iris is extracted from the unwanted parts of an iris image. The process of iris segmentation is mainly affected by the noise artefacts such as eyelid/eyelashes occlusions, specular reflections, intensity inhomogeneities, and non-circularity

Contents have been reproduced by permission of the publishers.
Springer 纳米技术权威期刊征稿
ACS ES&T Engineering
ACS ES&T Water
ACS Publications填问卷