Boosting palmprint identification with gender information using DeepNet

Chai, Tingting; Prasad, Shitala; Wang, Shenghui

doi:10.1016/j.future.2019.04.013

Cited by 38 publications

(16 citation statements)

References 29 publications

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“…The BJTU_PalmV1 dataset [30] is a compilation of contact-based palmprints, featuring 2431 hand images from 174 participants. This dataset encompasses a diverse group, with 77 males and 77 females all falling within the age range of 19 to 40 years.…”

Section: Bjtu_palmv1 (2019)mentioning

confidence: 99%

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Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Amrouni,

Benzaoui,

Zeroual

2023

Applied Sciences

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This paper presents a comprehensive survey examining the prevailing feature extraction methodologies employed within biometric palmprint recognition models. It encompasses a critical analysis of extant datasets and a comparative study of algorithmic approaches. Specifically, this review delves into palmprint recognition systems, focusing on different feature extraction methodologies. As the dataset wields a profound impact within palmprint recognition, our study meticulously describes 20 extensively employed and recognized palmprint datasets. Furthermore, we classify these datasets into two distinct classes: contact-based datasets and contactless-based datasets. Additionally, we propose a novel taxonomy to categorize palmprint recognition feature extraction approaches into line-based approaches, texture descriptor-based approaches, subspace learning-based methods, local direction encoding-based approaches, and deep learning-based architecture approaches. Within each class, most foundational publications are reviewed, highlighting their core contributions, the datasets utilized, efficiency assessment metrics, and the best outcomes achieved. Finally, open challenges and emerging trends that deserve further attention are elucidated to push progress in future research.

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Section: Bjtu_palmv1 (2019)mentioning

confidence: 99%

“…The BJTU_PalmV2 dataset [30] is a contactless palmprint collection featuring 2663 hand images from 148 volunteers, including 91 males and 57 females, spanning ages 8 to 73. Data were collected over two sessions from 2015 to 2017.…”

Section: Bjtu_palmv2 (2019)mentioning

confidence: 99%

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Amrouni,

Benzaoui,

Zeroual

2023

Applied Sciences

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“…Palm images binarization/hand and or fingers contour extraction: Palm Images binarization and hand/or fingers contour extraction and hand and or fingers contour extraction. There is a similarity in all preprocessing algorithms [9], [25], [28], [32], [48], [60], [68], [69] (b) Bisector-based approach: It builds lines using two points finger boundaries of gravity, and the midpoint of its starting point and endpoints with intersection considered a critical point [2], [22], [46], [51], [73], [74].…”

Section: Lowquality Imagesmentioning

confidence: 99%

“…Currently, most level 3 feature acquisition, extraction, and matching techniques are aimed at identifying fingerprints. Palm Code 2D-Gabor filter responses phase coding [48] Fusion Code 2D-Gabor filter responses phase calling with the maximum magnitude [10] Orientation Code Palm lines orientation information coding [9], [49] Text Descriptor Local binary pattern DCT coefficient coding…”

Section: Reference Approach Descriptionmentioning

confidence: 99%

Contactless Palmprint Recognition System: A Survey

et al. 2022

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Information systems in organizations traditionally require users to remember their secret pins (password), token, card number, or both to confirm their identities. However, the technological trend has been moving towards personal identification based on individual behavioural attributes (such as gaits, signature, and voice) or physiological (such as palmprint, fingerprint, face, iris, or ear). These attributes (biometrics) offer many advantages over knowledge and possession-based approaches. For example, palmprint images have rich, unique features for reliable human identification, and it has received significant attention due to their stability, reliability, uniqueness, and non-intrusiveness. This paper provides an overview and evaluation of contactless palmprint recognition system, the state-of-the-art performance of existing works, different types of "Region of Interest" (ROI) extraction algorithms, feature extraction, and matching algorithms. Finally, the findings obtained are presented and discussed.INDEX TERMS biometrics, information system, palmprint images, region of interest

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“…Matkowski et al [25] proposed a CNN framework for palmprint recognition in an uncontrollable environment, which included two sub-networks for segmenting region of interest (ROI) and extracting features, respectively. Chai et al [26] pre-trained a network with gender soft biometric, and then trained the network for palmprint classification. Du et al [27] proposed a CNNbased regularized adversarial domain model for crossdomain recognition.…”

Section: B Training Network For Palmprint/palmvein Recognitionmentioning

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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Boosting palmprint identification with gender information using DeepNet

Cited by 38 publications

References 29 publications

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Contactless Palmprint Recognition System: A Survey

Palmprint-Palmvein Fusion Recognition Based on Deep Hashing Network

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