Palmprint and Dorsal Hand Vein Dualmodal Biometrics

Zhong, Dexing; Li, Menghan; Shao, Huikai; Liu, Shuming

doi:10.1109/icmew.2018.8551582

Cited by 17 publications

(6 citation statements)

References 23 publications

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“…Even in biometric systems, the employment of deep learning has seen a significant surge and is producing very good results in comparison to the other methods that have been used previously (Summary in Table 5). Zhong et al (2018) proposed the use of DHN (deep hashing network) for palm print encoding into 128-bit codes, and BGM (biometric graph matching) to encode dorsal hand vein images into three discriminant features. Later, feature level fusion was used with very good recognition rates, with EER of upto 0%.…”

Section: Deep Featuresmentioning

confidence: 99%

Hand-based multibiometric systems: state-of-the-art and future challenges

Aftab

Khan

et al. 2021

PeerJ Computer Science

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The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

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Section: Deep Featuresmentioning

confidence: 99%

Hand-based multibiometric systems: state-of-the-art and future challenges

Aftab

Khan

et al. 2021

PeerJ Computer Science

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“…For different palmprint modalities, different methods have been proposed e.g. 2D palmprint recognition [2,3], 3D palmprint recognition [4], palmprint and vein recognition [5,6], etc. The existing methods can be briefly divided into the subspace-based [7][8][9], statistic-based [10][11][12], deep-learningbased [13,14] and coding-based methods [15,16].…”

Section: Introductionmentioning

confidence: 99%

Palmprint-Palmvein Fusion Recognition Based on Deep Hashing Network

Wu¹,

Leng

Khan

et al. 2021

IEEE Access

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Palmprint attracts increasing attention due to its several advantages in the biometrics field. Deep learning has achieved remarkable performance in the computer vision area, so a large number of deep-learning-based methods have been proposed by the research community for palmprint recognition. The outputs of a deep hashing network (DHN) can be represented as binary bit string, so DHN can reduce the storage and accelerate matching/retrieval speed. In this paper, DHN is employed to extract the binary template for palmprint and palmvein verification. Spatial transformer network is used to overcome the rotation and dislocation. Palmprint and palmvein can be acquired from visible-light spectrums, including red (R), green (G), blue (B), and near infrared (NIR) spectrum, respectively. Since the features in different spectrums are different, their complementary advantages can be exploited to the full by fusion. Image-level fusion and score-level fusion are developed for palmprint-palmvein fusion recognition. The sufficient experiments demonstrate that score-level fusion can improve the accuracy efficiently.

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“…It is usually accepted before feature drawing out to reduce the noise influence on the consequent feature abstraction and to increase enactment. Toward adapt the main patterns into the necessary designs, the pre-processing operation will be approved with on the original patterns [10][11][12]. Depending on [13][14][15][16] deep learning techniques have been supported in image classification and responsibilities recovery.…”

Section: Introductionmentioning

confidence: 99%