Contactless Palmprint Recognition Research Articles

Optimizing CNNs for Contactless Palmprint Recognition

Palm printing and palm vein recognition are the newer spheres within the already developed biometrics sector. Although many time-honored techniques have been offered and seen successful implementation over the last twenty years, the deep learning methods still lack all-round development in the palmprint and palm vein recognition. This research intends to study further the strength of deep learning on palmiets and palm vein recognition via 2D and 3D. We performed thorough examination of 17 known convolutional neural networks (CNNs) utilizing multiple databases, e.g., one 3D palmprint database, five 2D palmprint database and two palm vein databases. Our trials cover various network architectures, learning rates, and layer configurations, taking into account not only single mode data but also mixed mode data simultaneously. Results prove that CNNs of classic format show good recognition capabilities, and recent models with improved accuracy show even better achievements. One of the classic CNNs that stands out is Efficient Net. If the recognition accuracy is evaluated, this is the top performer. On the other hand, even though the classic forms of CNNs are fairly good in recognizing various types of tumors, their accuracy is still lower than the traditional methods. “Abstract” is a necessary section in a research paper. It may be constructed by gathering main points (summary) from each section of the research paper.

A Self-Attention CycleGAN for Cross-Domain Semi-Supervised Contactless Palmprint Recognition

Nowadays, there is a growing concern about contactless palmprint recognition because of its high-recognition rate, efficiency, and convenience. With the development of image acquisition equipment, it is an often case that the palmprint images for identification and for registration are captured by different devices. At the same time, a large amount of well-labeled palmprint images are difficult to collect. Therefore, the performance of most existing contactless palmprint recognition methods will be poor in real-life applications. To address these issues, we proposed a self-attention CycleGAN for cross-domain semi-supervised palmprint recognition. Based on CycleGAN, the styles of contactless palmprint images in source domain and target domain can be swapped. Specifically, the spatial features are captured through self-attention modules by modeling long-range dependencies. In addition, an extra source domain classifier is trained with the labeled source domain images to give the unlabeled images in target domain a pseudo-label, by which images in target domain are efficiently utilized. The experiment results showed that our method achieved competitive performance.

BEST: Building evidences from scattered templates for accurate contactless palmprint recognition

Contactless palmprint identification offers significantly improved hygiene and user convenience, making it highly attractive for a range of civilian applications, especially during the current pandemic. However, the accurate recognition of contactless palmprint images can be highly challenging, attributed to the significant variations in the intra-class similarity and limitations of conventional palmprint feature descriptors under involuntary or contactless imaging variations. State-of-the-art completely contactless palmprint matching algorithms in the literature cannot adequately address these challenges and are not sufficiently accurate and fast enough for such real-world applications. This paper proposes a novel approach that adaptively locates the local palmprint regions with high similarities between their corresponding feature representations or templates to address these challenges. We consider spatial localization of such highly similar feature representations from multiple local regions and consolidate them to generate a more reliable match score. This paper presents reproducible and comparative experimental results, using within-database, cross-database, and cross-sensor performance evaluation, on four publicly available contactless palmprint datasets, including a sizeable contactless palmprint database from 600 different subjects. The proposed method achieves outperforming results compared with three state-of-the-art deep learning-based methods and five widely used conventional methods. In addition, the proposed method is also significantly faster than all state-of-the-art baseline methods.

Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis.

In recent years, palmprint recognition has gained increased interest and has been a focus of significant research as a trustworthy personal identification method. The performance of any palmprint recognition system mainly depends on the effectiveness of the utilized feature extraction approach. In this paper, we propose a three-step approach to address the challenging problem of contactless palmprint recognition: (1) a pre-processing, based on median filtering and contrast limited adaptive histogram equalization (CLAHE), is used to remove potential noise and equalize the images' lighting; (2) a multiresolution analysis is applied to extract binarized statistical image features (BSIF) at several discrete wavelet transform (DWT) resolutions; (3) a classification stage is performed to categorize the extracted features into the corresponding class using a K-nearest neighbors (K-NN)-based classifier. The feature extraction strategy is the main contribution of this work; we used the multiresolution analysis to extract the pertinent information from several image resolutions as an alternative to the classical method based on multi-patch decomposition. The proposed approach was thoroughly assessed using two contactless palmprint databases: the Indian Institute of Technology-Delhi (IITD) and the Chinese Academy of Sciences Institute of Automatisation (CASIA). The results are impressive compared to the current state-of-the-art methods: the Rank-1 recognition rates are 98.77% and 98.10% for the IITD and CASIA databases, respectively.

CR Loss: Improving Biometric Using ClassRoom Learning Approach

Abstract One of the important factors in deep feature learning is their loss function design which highly influences the network performance. In this paper, we proposed a classroom (CR) learning approach along with arcface loss for contactless palmprint recognition to obtain high-level discriminative features without any extra load made to the network architecture. CR loss allows the network to learn the best possible feature representations for palmprint images. To validate our concept, we performed extensive experimental evaluations on various popular benchmark palmprint databases where our methods outperform the state-of-the-art methods. We also introduced a challenging contactless palmprint database called Harbin Institute of Technology-Network & Information Security Research Center contactless palmprint database version 1.0 (HIT-NIST-V1), as a new contribution to this domain. The result proves that the proposed CR loss consistently outperforms the SOTA methods for all the considered databases and especially for HIT-NIST-V1.

Innovative Contactless Palmprint Recognition System Based on Dual-Camera Alignment

Recently, contactless bimodal palmprint recognition technology has attracted increased attention due to the COVID-19 pandemic. Many dual-camera-based sensors have been proposed to capture palm vein and palmprint images synchronously. However, translations between captured palmprint and palm vein images differ depending on the distance between the hand and the sensors. To address this issue, we designed a low-cost method to align the bimodal palm regions for current dual-camera systems. In this study, we first implemented a contactless palm image acquisition device with a dual-camera module and a single-point time of flight (TOF) ranging sensor. Using this device, we collected a dataset named DCPD under different distances and light source intensities from 271 different palms. Then, a bimodal palm image alignment method is proposed based on the imaging and ranging models. After the system model is calibrated, the translation between the visible light and infrared light palm regions can be estimated quickly based on the palm distance. Finally, we designed a convolutional neural network (CNN) to effectively extract the fine- and coarse-grained palm features. Compared to widely used existing methods, the proposed networks achieved the lowest equal error rate (EER) on the Tongji, IITD, and DCPD datasets, and the average time cost of the system to perform one-time identification is approximately 0.15 s. The experimental results indicate that the proposed methods achieved high efficiency and comparable accuracy. In addition, the system’s EER and rank-1 on the DCPD dataset were 0.304% and 98.66%, respectively.

Contactless Palmprint Recognition System: A Survey

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.

Learning to Combine Local and Global Image Information for Contactless Palmprint Recognition.

In the past few years, there has been a leap from traditional palmprint recognition methodologies, which use handcrafted features, to deep-learning approaches that are able to automatically learn feature representations from the input data. However, the information that is extracted from such deep-learning models typically corresponds to the global image appearance, where only the most discriminative cues from the input image are considered. This characteristic is especially problematic when data is acquired in unconstrained settings, as in the case of contactless palmprint recognition systems, where visual artifacts caused by elastic deformations of the palmar surface are typically present in spatially local parts of the captured images. In this study we address the problem of elastic deformations by introducing a new approach to contactless palmprint recognition based on a novel CNN model, designed as a two-path architecture, where one path processes the input in a holistic manner, while the second path extracts local information from smaller image patches sampled from the input image. As elastic deformations can be assumed to most significantly affect the global appearance, while having a lesser impact on spatially local image areas, the local processing path addresses the issues related to elastic deformations thereby supplementing the information from the global processing path. The model is trained with a learning objective that combines the Additive Angular Margin (ArcFace) Loss and the well-known center loss. By using the proposed model design, the discriminative power of the learned image representation is significantly enhanced compared to standard holistic models, which, as we show in the experimental section, leads to state-of-the-art performance for contactless palmprint recognition. Our approach is tested on two publicly available contactless palmprint datasets—namely, IITD and CASIA—and is demonstrated to perform favorably against state-of-the-art methods from the literature. The source code for the proposed model is made publicly available.

HandSegNet: Hand segmentation using convolutional neural network for contactless palmprint recognition

Extracting a palm region with fixed location from an input hand image is a crucial task for palmprint recognition to realise reliable person authentication under contactless and unconstrained conditions. A palm region can be extracted from the fixed location using the gaps between fingers. An accurate and robust hand segmentation method is indispensable to extract a palm region from an image with complex background taken under various environments. In this study, HandSegNet, which is a hand segmentation method using Convolutional Neural Network (CNN) for contactless palmprint recognition, is proposed. HandSegNet employs a new CNN architecture consisting of an encoder–decoder model with a pyramid pooling module. Through performance evaluation using a set of synthesised hand images, HandSegNet exhibited the best segmentation results of 98.90% and 93.20% for accuracy and intersection over union, respectively. The effectiveness of HandSegNet in contactless palmprint recognition through experiments using a set of synthesised images of hand images is also demonstrated. Comparing the performance of palmprint recognition using three conventional methods and HandSegNet for palm region extraction, the proposed method has the lowest equal error rate of 4.995%, demonstrating its effectiveness in palm region extraction for contactless palmprint recognition.

CompNet: Competitive Neural Network for Palmprint Recognition Using Learnable Gabor Kernels

Contactless palmprint recognition has recently made significant progress in palm-scanning payment and social security. However, most existing methods are based on handcrafted kernels and are sensitive to illumination and scale variations. To address this problem, a competitive convolutional neural network (CompNet) with constrained learnable Gabor filters is proposed for contactless palmprint recognition. The proposed CompNet is built on multisize competitive blocks, which are applied to effectively exploit the rich direction ordering information of the palmprint patterns by means of the ad-hoc softmax and channel-wise convolution operations. Compared to the current deep neural networks, the backbone of the proposed network contains only very few parameters, making it quite easy to train, especially on small-scale datasets. Experimental results obtained on four popular contactless palmprint datasets demonstrate that the proposed CompNet achieves the lowest equal error rate compared to the most commonly used methods.

Deep discriminative representation for generic palmprint recognition

State-of-the-art palmprint recognition methods have achieved significant performances. However, most of the existing methods are focused on particular scenarios such as a specific illumination or being captured using a contact-based or contactless device. Therefore, these algorithms cannot meet the ever-changing complex application requirements. To resolve this issue, this paper proposes a generic framework to represent high-level discriminative features for multiple scenarios in palmprint recognition with learned discriminative deep convolutional networks named deep discriminative representation (DDR). We propose to learn discriminative deep convolutional networks with limited palmprint training data, which is utilized to extract deep discriminative features. Then, the collaborative representation based classifier is implemented for palmprint recognition, which is flexible and practical in numerous scenarios. The experimental results demonstrate that DDR produces the best recognition performance in generic palmprint recognition compared to other state-of-the-art methods. For contact-based palmprint recognition under different lighting sources, DDR achieved the best performance on the PolyU Multi-spectral database with M_R, M_B, M_G and M_NIR, respectively. As for contactless palmprint recognition, DDR obtained the highest results on the IITD and CASIA databases.

Masked SIFT with align‐based refinement for contactless palmprint recognition

Contactless palmprint is considered more user convenient than other biometrics due to its acquisition simplicity and less-private nature. Many challenges arise which affect the performance of common contact-based methods when applied to a contactless environment. For example, pose and illumination variations affect the layout and visibility of palm lines. This study proposes a SIFT-based method with three main modifications from the traditional SIFT. First, the palm regions with no significant lines/wrinkles are masked out to reduce the false features. A region with multi-lines is then described by multi-descriptors rather than a single one. Second, only query and target keypoints with small rotation difference are compared together, instead of comparing them all. This speed-up the comparison and enhance the accuracy, versus SIFT, by reducing the wrong matches. Third, an align-based refinement is applied to filter out the incorrect matches. The method is tested on three contactless hand databases; IITD, GPDS and Sfax-Miracl. It achieves a verification equal error rate of 0.72, 0.84 and 1.14% and a correct identification rate of 98.9, 99 and 98.9% on each database, respectively. These results are significantly better than the state-of-art methods on the same databases by 1.9% for verification and 3.2% for identification.

Towards contactless palmprint recognition: A novel device, a new benchmark, and a collaborative representation based identification approach

Biometric authentication has been found to be an effective method for recognizing a person’s identity with a high confidence. In this field, the use of palmprint represents a recent trend. To make the palmprint-based recognition systems more user-friendly and sanitary, researchers have been investigating how to design such systems in a contactless manner. Though substantial effort has been devoted to this area, it is still not quite clear about the discriminant power of the contactless palmprint, mainly owing to lack of a public, large-scale, and high-quality benchmark dataset collected using a well-designed device. As an attempt to fill this gap, we have at first developed a highly user-friendly device for capturing high-quality contactless palmprint images. Then, with the developed device, a large-scale palmprint image dataset is established, comprising 12,000 images collected from 600 different palms in two separate sessions. To the best of our knowledge, it is the largest contactless palmprint image benchmark dataset ever collected. Besides, for the first time, the quality of collected images is analyzed using modern image quality assessment metrics. Furthermore, for contactless palmprint identification, we have proposed a novel approach, namely CR_CompCode, which can achieve high recognition accuracy while having an extremely low computational complexity. To make the results fully reproducible, the collected dataset and the related source codes are publicly available at http://sse.tongji.edu.cn/linzhang/contactlesspalm/index.htm.

Low-rank representation integrated with principal line distance for contactless palmprint recognition

Contactless palmprint recognition has recently begun to draw attention of researchers. Different from conventional palmprint images, contactless palmprint images are captured under free conditions and usually have significant variations on translations, rotations, illuminations and even backgrounds. Conventional powerful palmprint recognition methods are not very effective for the recognition of contactless palmprint. It is known that low-rank representation (LRR) is a promizing scheme for subspace clustering, owing to its success in exploring the multiple subspace structures of data. In this paper, we integrate LRR with the adaptive principal line distance for contactless palmprint recognition. The principal lines are the most distinctive features of the palmprint and can be correctly extracted in most cases; thereby, the principal line distances can be used to determine the neighbors of a palmprint image. With the principal line distance penalty, the proposed method effectively improves the clustering results of LRR by improving the weights of the affinities among nearby samples with small principal line distances. Therefore, the weighted affinity graph identified by the proposed method is more discriminative. Extensive experiments show that the proposed method can achieve higher accuracy than both the conventional powerful palmprint recognition methods and the subspace clustering-based methods in contactless palmprint recognition. Also, the proposed method shows promizing robustness to the noisy palmprint images. The effectiveness of the proposed method indicates that using LRR for contactless palmprint recognition is feasible.

Dual-source discrimination power analysis for multi-instance contactless palmprint recognition

Due to the benefits of palmprint recognition and the advantages of biometric fusion systems, it is necessary to study multi-source palmprint fusion systems. Unfortunately, the research on multi-instance palmprint feature fusion is absent until now. In this paper, we extract the features of left and right palmprints with two-dimensional discrete cosine transform (2DDCT) to constitute a dual-source space. Normalization is utilized in dual-source space to avoid the disturbance caused by the coefficients with large absolute values. Thus complicated pre-masking is needless and arbitrary removing of discriminative coefficients is avoided. Since more discriminative coefficients can be preserved and retrieved with discrimination power analysis (DPA) from dual-source space, the accuracy performance is improved. The experiments performed on contactless palmprint database confirm that dual-source DPA, which is designed for multi-instance palmprint feature fusion recognition, outperforms single-source DPA.

KLT 특징점에 기반한 비접촉 장문인식

비접촉 장문을 인식하기 위해서는 영상의 크기 및 회전 변형을 효과적으로 해결해야 한다. 본 연구에서는 손의 크기와 방향에 따라 관심영역(ROI)을 추출한 후 정규화하여 일차적으로 이러한 변형을 최소화하였다. 본 논문에서는 KLT(Kanade-Lukas-Tomasi) 특징점에 기반한 비접촉 장문인식 방법을 제안한다. 대응되는 KLT 특징점 주위의 국소영역에 대한 텍스처를 비교하여 대응되는 특징점을 검출한 후, 특징점 쌍의 변위 크기와 방향을 나타내는 변위벡터들 간의 유사도를 비교하여 장문을 인식한다. CASIA 공개 데이터베이스를 이용한 실험결과 제안된 방법이 비접촉 장문인식에 효과적임을 확인할 수 있었다. 특히 다중 가버 필터를 이용하였을 때 99%를 상회하는 정인식률을 얻을 수 있었다. An effective solution to the variation on scale and rotation is required to recognize contactless palmprint. In this study, we firstly minimize the variation by extracting a region of interest(ROI) according to the size and orientation of hand and normalizing the ROI. This paper proposes a contactless palmprint recognition method based on KLT(Kanade-Lukas-Tomasi) feature points. To detect corresponding feature points, texture in local regions around KLT feature points are compared. Then, we recognize palmprint by measuring the similarity among displacement vectors which represent the size and direction of displacement of each pair of corresponding feature points. An experimental results using CASIA public database show that the proposed method is effective in contactless palmprint recognition. Especially, we can get the performance of exceeding 99% correct identification rate using multiple Gabor filters.

A SIFT-based contactless palmprint verification approach using iterative RANSAC and local palmprint descriptors

Palmprint recognition is a relatively new and effective biometric technology. Most of the traditional palmprint recognition methods are based on contact acquisition devices, which affects their user-friendliness and limits their applications. To overcome these shortcomings, this work proposes a contactless palmprint verification approach based on SIFT, which is composed of three steps, namely, image preprocessing, SIFT feature extraction and matching, and matching refinement. Palmprint images are firstly preprocessed using an isotropic filter, and then the SIFT points are detected and matched. Finally, the matched points are refined by employing a two-stage strategy. In the first stage, an iterative RANSAC (I-RANSAC) algorithm is employed to remove the mis-matched points which fail to satisfy the topological relations. In the second stage, local palmprint descriptors (LPDs) are extracted for SIFT points to further remove the mis-matched points which cannot be distinguished by original SIFT descriptors. The number of final matched SIFT points is taken as the score for decision. Experimental results show that the proposed approach is effective in contactless palmprint recognition, especially when non-linear deformations exist in palmprint images.