3D Palmprint 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.

LDBM-PFR: Local Directional Binary Pattern Mixed Filtering for 2D and 3D Palmprint Fusion Recognition

<p>Single-dimensional palmprint images are susceptible to interference from noise, lighting, and various factors, leading to information loss and subpar recognition performance. To address the problem, a fusion recognition scheme of 2D and 3D palmprints based on Hybrid Filter Local Orientation Binary Pattern (HFLOBP) is proposed. Firstly, the HFLOBP algorithm is employed to extract 2D features and 3D Shape Index (SI) features respectively to improve the recognition effect. Secondly, Principal Component Analysis (PCA) is utilized to improve the recognition efficiency, to reduce the dimension of 2D features and 3D Surface Type (ST) features of palmprint, to improve the recognition efficiency. Finally, the multi-dimensional palmprint features are fused by Double Weight Threshold Algorithm (DWTA), and classified and recognized by Collaborative Representation (CR). Experimental results conducted on the 3D palmprint database of Hong Kong Polytechnic University reveal that, in comparison with alternative classification methods, the average recognition rate reaches 99.98%, with an average recognition time of 0.64 seconds. The proposed method not only demonstrates a commendable recognition effect but also meets the real-time requirements, underscoring its practical application value.</p> <p> </p>

Recognition Performance Analysis of a Multimodal Biometric System Based on the Fusion of 3D Ultrasound Hand-Geometry and Palmprint

Multimodal biometric systems are often used in a wide variety of applications where high security is required. Such systems show several merits in terms of universality and recognition rate compared to unimodal systems. Among several acquisition technologies, ultrasound bears great potential in high secure access applications because it allows the acquisition of 3D information about the human body and is able to verify liveness of the sample. In this work, recognition performances of a multimodal system obtained by fusing palmprint and hand-geometry 3D features, which are extracted from the same collected volumetric image, are extensively evaluated. Several fusion techniques based on the weighted score sum rule and on a wide variety of possible combinations of palmprint and hand geometry scores are experimented with. Recognition performances of the various methods are evaluated and compared through verification and identification experiments carried out on a homemade database employed in previous works. Verification results demonstrated that the fusion, in most cases, produces a noticeable improvement compared to unimodal systems: an EER value of 0.06% is achieved in at least five cases against values of 1.18% and 0.63% obtained in the best case for unimodal palmprint and hand geometry, respectively. The analysis also revealed that the best fusion results do not include any combination between the best scores of unimodal characteristics. Identification experiments, carried out for the methods that provided the best verification results, consistently demonstrated an identification rate of 100%, against 98% and 91% obtained in the best case for unimodal palmprint and hand geometry, respectively.

A fuzzy connective score fusion technique for 2D and 3D palmprint authentication system

A fuzzy connective score fusion technique for 2D and 3D palmprint authentication system

3D Vascular Pattern Extraction from Grayscale Volumetric Ultrasound Images for Biometric Recognition Purposes

Recognition systems based on palm veins are gaining increasing attention as they are highly distinctive and very hard to counterfeit. Most popular systems are based on infrared radiation; they have the merit to be contactless but can provide only 2D patterns. Conversely, 3D patterns can be achieved with Doppler or photoacoustic methods, but these approaches require too long of an acquisition time. In this work, a method for extracting 3D vascular patterns from conventional grayscale volumetric images of the human hand, which can be collected in a short time, is proposed for the first time. It is based on the detection of low-brightness areas in B-mode images. Centroids of these areas in successive B-mode images are then linked through a minimum distance criterion. Preliminary verification and identification results, carried out on a database previously established for extracting 3D palmprint features, demonstrated good recognition performances: EER = 2%, ROC AUC = 99.92%, and an identification rate of 100%. As further merit, 3D vein pattern features can be fused to 3D palmprint features to implement a costless multimodal recognition system.

Palmprint Phenotype Feature Extraction and Classification Based on Deep Learning.

Palmprints are of long practical and cultural interest. Palmprint principal lines, also called primary palmar lines, are one of the most dominant palmprint features and do not change over the lifespan. The existing methods utilize filters and edge detection operators to get the principal lines from the palm region of interest (ROI), but can not distinguish the principal lines from fine wrinkles. This paper proposes a novel deep-learning architecture to extract palmprint principal lines, which could greatly reduce the influence of fine wrinkles, and classify palmprint phenotypes further from 2D palmprint images. This architecture includes three modules, ROI extraction module (REM) using pre-trained hand key point location model, principal line extraction module (PLEM) using deep edge detection model, and phenotype classifier (PC) based on ResNet34 network. Compared with the current ROI extraction method, our extraction is competitive with a success rate of 95.2%. For principal line extraction, the similarity score between our extracted lines and ground truth palmprint lines achieves 0.813. And the proposed architecture achieves a phenotype classification accuracy of 95.7% based on our self-built palmprint dataset CAS_Palm.

Touchless palmprint recognition based on 3D Gabor template and block feature refinement

With the growing demand for hand hygiene, touchless palmprint recognition has made great strides recently, providing an effective solution for personal identification. Despite many efforts devoted to this area, the discriminative ability of touchless palmprint is still uncertain, especially for large-scale datasets. To address this problem, in this study, a large-scale touchless palmprint dataset was built containing 2334 palms from 1167 individuals. To the best of our knowledge, this is currently the largest touchless palmprint image benchmark regarding the number of individuals and palms. In addition, a novel deep learning framework was proposed for touchless palmprint recognition, referred to as 3D convolution palmprint recognition network (3DCPN), which leverages 3D convolutions to dynamically integrate multiple Gabor features. In 3DCPN, a novel variant of the Gabor filter is embedded into the first layer to enhance curve feature extraction. Using a well-designed ensemble scheme, low-level 3D features are convolved to extract high-level features. Finally, a region-based loss function is implemented to strengthen the discriminative ability of both global and local descriptors. Extensive experiments were conducted on the newly built dataset and other popular databases to demonstrate the superiority of our method. The results show that the proposed 3DCPN achieves state-of-the-art or comparable performance.

Multimodal Biometric Recognition Based on 3D Ultrasound Palmprint-Hand Geometry Fusion

In recent years, multimodal biometric systems are increasingly employed in many application field due to several advantages in terms of universality, recognition rate. and security. Among various acquisition technologies, Ultrasound shows important merits, because it allows obtaining volumetric images of the human body and hence a more accurate description of characteristics and to verify liveness. In this work, a multimodal ultrasound recognition system based on the fusion between 3D hand geometry and 3D palmprint features is proposed and experimentally evaluated. The system acquires a volumetric image of the whole hand and for both characteristics, several 2D images are extracted at different depth levels. From each image, 2D features are extracted and then properly combined to achieve a 3D template. Recognition performances are evaluated through verification and identification experiments by employing a homemade database. Experiments are carried out first for the two unimodal biometrics and successively, by fusing the two modalities at score level. Results have shown that fusion is able to dramatically improve the recognition performances of the single biometrics, achieving an Equal Error Rate of 0.08% and an identification rate of 100%.

3D palmprint recognition based on local orientation binary pattern combined with collaborative representation

3D palmprint recognition based on local orientation binary pattern combined with collaborative representation

2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these CNNs have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In order to overcome some shortcomings of manually designed CNN, neural architecture search (NAS) technology has become an important research direction of deep learning. The significance of NAS is to solve the deep learning model’s parameter adjustment problem, which is a cross-study combining optimization and machine learning. NAS technology represents the future development direction of deep learning. However, up to now, NAS technology has not been well studied for palmprint recognition and palm vein recognition. In this paper, in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases, two palm vein databases, and one 3D palmprint database. Experimental results show that some NAS methods can achieve promising recognition results. Remarkably, among different evaluated NAS methods, ProxylessNAS achieves the best recognition performance.

Accurate 3D Finger Knuckle Recognition Using Auto-Generated Similarity Functions

Contactless 3D finger knuckle is an emerging biometric identifier, which can provide a promising alternative for personal identification. To maximize its potential, feature representation and matching are the two critical components towards high performance. A recent pioneering work is limited by its preliminary design of the feature descriptor and the tailor-made similarity function. Although this method demonstrates decent recognition performance, there is room for improvement. This article advances the state-of-the-art method by introducing a new curvature based feature descriptor and a method to compute the similarity functions based on the statistical distribution of the encoded feature space. Our proposed feature representation utilizes an insight in 3D geometry for accurately encoding the curvature information. When computing the similarity between a pair of templates, we compute the similarity function from the probability mass distributions of the encoded feature space. Our proposed approach is scalable to templates with different sizes, and more importantly outperforms the state-of-the-art methods significantly, which is demonstrated in a publicly available database of 3D finger knuckle. In addition, we also demonstrate the generalizability of our approach by evaluating on other publicly available biometric datasets of similar patterns, i.e., 3D palmprint and finger vein.

A Feasible 3D Ultrasound Palmprint Recognition System for Secure Access Control Applications

Biometric recognition systems based on 3D palmprint captured with optical technology have been widely investigated in the last decade; however, they can provide information about the external skin surface only. This limit can be overcome by Ultrasound, which allows gaining information on the depth of palm lines and can verify the liveness of the sample, making the recognition systems very hard to fake. In this work, a feasible palmprint recognition system based on 3D ultrasound images is proposed. Unlike previous wet setups, the coupling between probe and human is realized through a gel pad, which permits a comfortable and precise positioning of the hand by the user. Collected 3D images are processed to generate 2D palmprint images at various under-skin depths. 2D features are then extracted from these images, experimenting with different procedures, and are merged to define a 3D template that contains lines’ depth information. Recognition performances were evaluated by performing verification and identification experiments on a home-made database composed of 423 samples from 55 volunteers. An EER rate of 0.36% and an identification accuracy of 100% are obtained. The suitability of the proposed system in secure access control applications is finally discussed.

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

Deep Feature Collaboration for Challenging 3D Finger Knuckle Identification

Contactless 3D finger knuckle pattern is a new biometric identifier which offers highly discriminative features for the finger knuckle based personal identification. State-of-the-art methods for object recognition, a more generic problem, employ deep neural network based approaches and demonstrate superior effectiveness. However, any direct applications from those methods do not outperform specialized hand-crafted feature description approaches for the problem addressed in this paper. In addition, such deep neural network based methods have to address challenges associated with emerging biometrics, e.g. availability of very limited training data, large intra-class or train-test sample variations as observed for the real applications, etc. This paper attempts to address the above challenges and introduces a new deep neural network based approach for the contactless 3D finger knuckle identification. Our approach simultaneously encodes and incorporates deep features from multiple scales to form a more robust deep feature representation. Such collaborative feature representations are robustly matched using an efficient alignment scheme with a fully convolutional architecture to accommodate involuntary finger variations during the contactless imaging. Comparative experimental results in the two-session 3D finger knuckle images database, acquired from over 200 subjects and is publicly introduced from this paper, illustrate superior performance over the state-of-the-art methods, e.g. offering ~22% GAR improvement at extremely low FAR under challenging comparison scenarios. Additional experiments in other publicly available databases including 3D palmprint, 3D fingerprint, and 2D finger knuckle further validate the effectiveness and demonstrate the generalizability of the proposed approach.

3D Palmprint recognition using Tan and Triggs normalization technique and GIST descriptors

The 3D palmprint biometric recognition system is being considered as a very promising way to address the limitations of 2D palmprint recognition systems. This paper introduces a novel method for the 3D palmprint recognition based on the Tan and Triggs normalization technique (TT) for the preprocessing data and GIST descriptor for feature extraction. The TT technique can effectively and efficiently eliminate not only the low frequencies containing the undesirable effects of shadows but also high frequencies containing aliasing and noise in 3D palmprint. The holistic feature extraction method has been employed to attain salient characteristics. Finally, for the features matching the cosine Mahalanobis distance has been used for 3D palmprint recognition. The proposed system has been evaluated on publicly available 3D palmprint database of 8000 samples. Experimental analyses show that the proposed method can functionally eradicate the effect of uneven illumination and greatly improve the performance of the recognition system. Moreover, experimental results demonstrate that our method is capable of competing with many existing state-of-the-art 3D palmprint recognition techniques as well as outperforming many others.

3D palmprint recognition using complete block wise descriptor

Recent years have witnessed a growing interesting in developing automatic 3D palmprint recognition systems. The key problem of 3D palmprint recognition is to contrive an effective representation. In this paper, we propose a 3D palmprint recognition method by completely fusing of multiple block wise features. First, we propose a shape index multi-direction binary code strategy to extract 2D texture-level features of 3D palmprint. Then, we use the compact surface type binary code scheme to characterize 3D structure-level features of 3D palmprint. Finally, we integrate them together to construct the complete block wise descriptor for 3D palmprint recognition. Experiments conducted on the public available 3D palmprint database validate the effectiveness of our proposed method.

A Performance Evaluation of Hashing Techniques for 2D and 3D Palmprint Retrieval and Recognition

Biometrics is one of the most effective solutions for personal authentication. With the wide use of biometrics technology, the scale of the biometrics database is increasing continuously. In this situation, fast matching and retrieval techniques become more and more necessary for large-scale biometrics recognition. Recently, palmprint recognition has become one of emerging biometrics technologies. Both low-resolution palmprint recognition and 3D palmprint recognition technologies were proposed and can be used for many civilian applications. However, the research on fast low-resolution palmprint and 3D palmprint retrieval is very preliminary. Hashing is one of the most popular techniques for image search due to its fast-computational speed and low storage cost. In this paper, we conduct a performance evaluation of hashing techniques for 2D and 3D palmprint retrieval and recognition. Several representative hashing methods are selected for performance evaluation including four unsupervised hashing methods, four supervised hashing methods, and four deep hashing methods. Experimental results demonstrate that some hashing methods such as column sampling based discrete supervised hashing (COSDISH) can obtain promising results.

3D palmprint identification using blocked histogram and improved sparse representation-based classifier

The three-dimensional (3D) palmprint-based biometrics has been an emerging approach for human recognition. However, it mainly concentrates on one-to-one verification and is not efficient for one-to-many identification. Especially, when there is a large number of samples in the database, obtaining both the accuracy and speed of recognition has been the major challenge. To handle these problems, we propose an improved sparse representation-based 3D palmprint identification scheme. The novelty of this investigation focuses on: (1) The histogram of blocked surface type and local phase of palmprint are integrated as one feature to reduce the dimensionality of 3D information and the complexity of the subsequent computations; and (2) by adding intermediate terms to the calculation of sparse coefficient, an improved sparse representation-based classifier is proposed to refine the classification results and enhance the accuracy of identification. Experimental studies demonstrate the effectiveness of the proposed scheme. Compared with other methods, the proposed solution can reduce the computational complexity and elevate the accuracy, speed, and robustness of identification. Moreover, the proposed 3D feature occupies small data volume, which makes the scheme more suitable for large-scale specimen identification applications.

Efficient and Accurate 3D Finger Knuckle Matching using Surface Key Points.

Contactless 3D finger knuckle is a new biometric identifier with a lot of potentials, which can provide an accurate, efficient and convenient alternative for the personal identification. The current 3D finger knuckle recognition methods are limited by computationally complex or inefficient matching algorithms, which attempt to compute the matching scores from all possible translational and rotational parameters for matching a pair of templates. The strength of such approach lies in its simplicity and reliability for accurately matching intra-class samples, but expensive computational time is required. Furthermore, attempting on excessive numbers of translational and rotational parameters can also degrade the overall recognition accuracy because the imposter matches can be increased. In fact, this conventional matching approach is commonly adopted in many biometric studies, but its drawbacks have not received adequate attention. This paper addresses such 3D finger knuckle recognition problem by developing a more efficient matching approach using surface key points extracted from 3D finger knuckle surfaces. Our comparative experimental results with the state-of-the art method on a publicly available 3D finger knuckle database indicates that our approach can offer over 23 times faster with performance improvement on the accuracy. Although the focus of our work is on 3D finger knuckle recognition, we also present the performance of our method on other publicly available databases with similar 3D biometric patterns including 3D palmprint and 3D fingerprint, to validate the effectiveness of the proposed approach.

3D palmprint recognition using unsupervised convolutional deep learning network and SVM classifier

Since past decade, efforts are afoot to design better hand-based automatic person recognition systems. Among the various hand-based biometric traits, palmprint as a biometric characteristic is now gaining increased attention from both the academic and industrial communities owing to its highly distinctive texture patterns, features richness, and stability. Here, the authors propose a new 3D palmprint recognition framework based on an unsupervised convolutional deep learning network named PCANet. Specifically, the proposed framework first reconstructs illumination-invariant 3D palmprint images using Single Scale Retinex (SSR) algorithm. Then, PCANet topology is employed to extract discriminative features from SSR images. Finally, a multi-class support vector machine (SVM) classification scheme is utilised to determine the identity of the person. Extensive experimental analysis on publicly available 3D palmprint PolyU dataset, which is composed of 8000 range images from 200 individuals, shows that proposed method outperforms existing approaches and is also able to attain 99.98% rank-1 accuracy.