92 research outputs found

    Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network

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    Fingerphoto images captured using a smartphone are successfully used to verify the individuals that have enabled several applications. This work presents a novel algorithm for fingerphoto verification using a nested residual block: Finger-NestNet. The proposed Finger-NestNet architecture is designed with three consecutive convolution blocks followed by a series of nested residual blocks to achieve reliable fingerphoto verification. This paper also presents the interpretability of the proposed method using four different visualization techniques that can shed light on the critical regions in the fingerphoto biometrics that can contribute to the reliable verification performance of the proposed method. Extensive experiments are performed on the fingerphoto dataset comprised of 196 unique fingers collected from 52 unique data subjects using an iPhone6S. Experimental results indicate the improved verification of the proposed method compared to six different existing methods with EER = 1.15%.Comment: a preprint paper accepted in wacv2023 worksho

    Fingervein Verification using Convolutional Multi-Head Attention Network

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    Biometric verification systems are deployed in various security-based access-control applications that require user-friendly and reliable person verification. Among the different biometric characteristics, fingervein biometrics have been extensively studied owing to their reliable verification performance. Furthermore, fingervein patterns reside inside the skin and are not visible outside; therefore, they possess inherent resistance to presentation attacks and degradation due to external factors. In this paper, we introduce a novel fingervein verification technique using a convolutional multihead attention network called VeinAtnNet. The proposed VeinAtnNet is designed to achieve light weight with a smaller number of learnable parameters while extracting discriminant information from both normal and enhanced fingervein images. The proposed VeinAtnNet was trained on the newly constructed fingervein dataset with 300 unique fingervein patterns that were captured in multiple sessions to obtain 92 samples per unique fingervein. Extensive experiments were performed on the newly collected dataset FV-300 and the publicly available FV-USM and FV-PolyU fingervein dataset. The performance of the proposed method was compared with five state-of-the-art fingervein verification systems, indicating the efficacy of the proposed VeinAtnNet.Comment: Accepted in IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 202

    Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey

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    The vulnerabilities of fingerprint authentication systems have raised security concerns when adapting them to highly secure access-control applications. Therefore, Fingerprint Presentation Attack Detection (FPAD) methods are essential for ensuring reliable fingerprint authentication. Owing to the lack of generation capacity of traditional handcrafted based approaches, deep learning-based FPAD has become mainstream and has achieved remarkable performance in the past decade. Existing reviews have focused more on hand-cratfed rather than deep learning-based methods, which are outdated. To stimulate future research, we will concentrate only on recent deep-learning-based FPAD methods. In this paper, we first briefly introduce the most common Presentation Attack Instruments (PAIs) and publicly available fingerprint Presentation Attack (PA) datasets. We then describe the existing deep-learning FPAD by categorizing them into contact, contactless, and smartphone-based approaches. Finally, we conclude the paper by discussing the open challenges at the current stage and emphasizing the potential future perspective.Comment: 29 pages, submitted to ACM computing survey journa

    3D Face Morphing Attacks: Generation, Vulnerability and Detection

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    Face Recognition systems (FRS) have been found to be vulnerable to morphing attacks, where the morphed face image is generated by blending the face images from contributory data subjects. This work presents a novel direction for generating face-morphing attacks in 3D. To this extent, we introduced a novel approach based on blending 3D face point clouds corresponding to contributory data subjects. The proposed method generates 3D face morphing by projecting the input 3D face point clouds onto depth maps and 2D color images, followed by image blending and wrapping operations performed independently on the color images and depth maps. We then back-projected the 2D morphing color map and the depth map to the point cloud using the canonical (fixed) view. Given that the generated 3D face morphing models will result in holes owing to a single canonical view, we have proposed a new algorithm for hole filling that will result in a high-quality 3D face morphing model. Extensive experiments were conducted on the newly generated 3D face dataset comprising 675 3D scans corresponding to 41 unique data subjects and a publicly available database (Facescape) with 100 data subjects. Experiments were performed to benchmark the vulnerability of the {proposed 3D morph-generation scheme against} automatic 2D, 3D FRS, and human observer analysis. We also presented a quantitative assessment of the quality of the generated 3D face-morphing models using eight different quality metrics. Finally, we propose three different 3D face Morphing Attack Detection (3D-MAD) algorithms to benchmark the performance of 3D face morphing attack detection techniques.Comment: The paper is accepted at IEEE Transactions on Biometrics, Behavior and Identity Scienc

    Deep Composite Face Image Attacks: Generation, Vulnerability and Detection

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    Face manipulation attacks have drawn the attention of biometric researchers because of their vulnerability to Face Recognition Systems (FRS). This paper proposes a novel scheme to generate Composite Face Image Attacks (CFIA) based on the Generative Adversarial Networks (GANs). Given the face images from contributory data subjects, the proposed CFIA method will independently generate the segmented facial attributes, then blend them using transparent masks to generate the CFIA samples. { The primary motivation for CFIA is to utilize deep learning to generate facial attribute-based composite attacks, which has been explored relatively less in the current literature.} We generate 1414 different combinations of facial attributes resulting in 1414 unique CFIA samples for each pair of contributory data subjects. Extensive experiments are carried out on our newly generated CFIA dataset consisting of 1000 unique identities with 2000 bona fide samples and 14000 CFIA samples, thus resulting in an overall 16000 face image samples. We perform a sequence of experiments to benchmark the vulnerability of CFIA to automatic FRS (based on both deep-learning and commercial-off-the-shelf (COTS). We introduced a new metric named Generalized Morphing Attack Potential (GMAP) to benchmark the vulnerability effectively. Additional experiments are performed to compute the perceptual quality of the generated CFIA samples. Finally, the CFIA detection performance is presented using three different Face Morphing Attack Detection (MAD) algorithms. The proposed CFIA method indicates good perceptual quality based on the obtained results. Further, { FRS is vulnerable to CFIA} (much higher than SOTA), making it difficult to detect by human observers and automatic detection algorithms. Lastly, we performed experiments to detect the CFIA samples using three different detection techniques automatically

    Reliable Face Morphing Attack Detection in On-The-Fly Border Control Scenario with Variation in Image Resolution and Capture Distance

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    Face Recognition Systems (FRS) are vulnerable to various attacks performed directly and indirectly. Among these attacks, face morphing attacks are highly potential in deceiving automatic FRS and human observers and indicate a severe security threat, especially in the border control scenario. This work presents a face morphing attack detection, especially in the On-The-Fly (OTF) Automatic Border Control (ABC) scenario. We present a novel Differential-MAD (D-MAD) algorithm based on the spherical interpolation and hierarchical fusion of deep features computed from six different pre-trained deep Convolutional Neural Networks (CNNs). Extensive experiments are carried out on the newly generated face morphing dataset (SCFace-Morph) based on the publicly available SCFace dataset by considering the real-life scenario of Automatic Border Control (ABC) gates. Experimental protocols are designed to benchmark the proposed and state-of-the-art (SOTA) D-MAD techniques for different camera resolutions and capture distances. Obtained results have indicated the superior performance of the proposed D-MAD method compared to the existing methods.Comment: The paper is accepted at the International Joint Conference on Biometrics (IJCB) 202

    Sound-Print: Generalised Face Presentation Attack Detection using Deep Representation of Sound Echoes

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    Facial biometrics are widely deployed in smartphone-based applications because of their usability and increased verification accuracy in unconstrained scenarios. The evolving applications of smartphone-based facial recognition have also increased Presentation Attacks (PAs), where an attacker can present a Presentation Attack Instrument (PAI) to maliciously gain access to the application. Because the materials used to generate PAI are not deterministic, the detection of unknown presentation attacks is challenging. In this paper, we present an acoustic echo-based face Presentation Attack Detection (PAD) on a smartphone in which the PAs are detected based on the reflection profiles of the transmitted signal. We propose a novel transmission signal based on the wide pulse that allows us to model the background noise before transmitting the signal and increase the Signal-to-Noise Ratio (SNR). The received signal reflections were processed to remove background noise and accurately represent reflection characteristics. The reflection profiles of the bona fide and PAs are different owing to the different reflection characteristics of the human skin and artefact materials. Extensive experiments are presented using the newly collected Acoustic Sound Echo Dataset (ASED) with 4807 samples captured from bona fide and four different types of PAIs, including print (two types), display, and silicone face-mask attacks. The obtained results indicate the robustness of the proposed method for detecting unknown face presentation attacks.Comment: Accepted in IJCB 202

    Differential Newborn Face Morphing Attack Detection using Wavelet Scatter Network

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    Face Recognition System (FRS) are shown to be vulnerable to morphed images of newborns. Detecting morphing attacks stemming from face images of newborn is important to avoid unwanted consequences, both for security and society. In this paper, we present a new reference-based/Differential Morphing Attack Detection (MAD) method to detect newborn morphing images using Wavelet Scattering Network (WSN). We propose a two-layer WSN with 250 ×\times 250 pixels and six rotations of wavelets per layer, resulting in 577 paths. The proposed approach is validated on a dataset of 852 bona fide images and 2460 morphing images constructed using face images of 42 unique newborns. The obtained results indicate a gain of over 10\% in detection accuracy over other existing D-MAD techniques.Comment: accepted in 5th International Conference on Bio-engineering for Smart Technologies (BIO-SMART 2023
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