Search Results (34)

Palmprint recognition is becoming more and more common in the fields of security authentication, mobile payment, and crime detection. Aiming at the problem of small sample size and low recognition rate of palmprint, a small-sample palmprint recognition method based on image expansion and Dynamic Model-Agnostic Meta-Learning (DMAML) is proposed. In terms of data augmentation, a multi-connected conditional generative network is designed for generating palmprints; the network is trained using a gradient-penalized hybrid loss function and a dual time-scale update rule to help the model converge stably, and the trained network is used to generate an expanded dataset of palmprints. On this basis, the palmprint feature extraction network is designed considering the frequency domain and residual inspiration to extract the palmprint feature information. The DMAML training method of the network is investigated, which establishes a multistep loss list for query ensemble loss in the inner loop. It dynamically adjusts the learning rate of the outer loop by using a combination of gradient preheating and a cosine annealing strategy in the outer loop. The experimental results show that the palmprint dataset expansion method in this paper can effectively improve the training efficiency of the palmprint recognition model, evaluated on the Tongji dataset in an N-way K-shot setting, our proposed method achieves an accuracy of 94.62% ± 0.06% in the 5-way 1-shot task and 87.52% ± 0.29% in the 10-way 1-shot task, significantly outperforming ProtoNets (90.57% ± 0.65% and 81.15% ± 0.50%, respectively). Under the 5-way 1-shot condition, there was a 4.05% improvement, and under the 10-way 1-shot condition, there was a 6.37% improvement, demonstrating the effectiveness of our method. Full article

Current finger-vein or palm-vein recognition systems usually require direct contact of the subject with the apparatus. This can be problematic in environments where hygiene is of primary importance. In this work we present a contactless vascular biometrics sensor platform named sweet which can be used for hand vascular biometrics studies (wrist, palm, and finger-vein) and surface features such as palmprint. It supports several acquisition modalities such as multi-spectral Near-Infrared (NIR), RGB-color, Stereo Vision (SV) and Photometric Stereo (PS). Using this platform we collected a dataset consisting of the fingers, palm and wrist vascular data of 120 subjects. We present biometric experimental results, focusing on Finger-Vein Recognition (FVR). Finally, we discuss fusion of multiple modalities. The acquisition software, parts of the hardware design, the new FV dataset, as well as source-code for our experiments are publicly available for research purposes. Full article

What if you could identify someone’s right palmprint just by looking at their left—and vice versa? That is exactly what I set out to do. I built a specially adapted Siamese network that only needs one palm to reliably recognize the other, making biometric systems far more flexible in everyday settings. My solution rests on two simple but powerful ideas. First, Anchor Embedding through Feature Aggregation (AnchorEFA) creates a “super-anchor” by averaging four palmprint samples from the same person. This pooled anchor smooths out noise and highlights the consistent patterns shared between left and right palms. Second, I use a Concatenated Similarity Measurement—combining Euclidean distance with Element-wise Absolute Difference (EAD)—so the model can pick up both big structural similarities and tiny textural differences. I tested this approach on three public datasets (POLYU_Left_Right, TongjiS1_Left_Right, and CASIA_Left_Right) and saw a clear jump in accuracy compared to traditional methods. In fact, my four-sample AnchorEFA plus hybrid similarity metric did not just beat the baseline—it set a new benchmark for cross-palmprint recognition. In short, recognizing a palmprint from its opposite pair is not just feasible—it is practical, accurate, and ready for real-world use. This work opens the door to more secure, user-friendly biometric systems that still work even when only one palmprint is available. Full article

Deep learning models have demonstrated significant advantages over traditional algorithms in image processing tasks like object detection. However, a large amount of data are needed to train such deep networks, which limits their application to tasks such as biometric recognition that require more training samples for each class (i.e., each individual). Researchers developing such complex systems rely on real biometric data, which raises privacy concerns and is restricted by the availability of extensive, varied datasets. This paper proposes a generative adversarial network (GAN)-based solution to produce training data (palm images) for improved biometric (palmprint-based) recognition systems. We investigate the performance of the most recent StyleGAN models in generating a thorough contactless palm image dataset for application in biometric research. Training on publicly available H-PolyU and IIDT palmprint databases, a total of 4839 images were generated using StyleGAN models. SIFT (Scale-Invariant Feature Transform) was used to find uniqueness and features at different sizes and angles, which showed a similarity score of 16.12% with the most recent StyleGAN3-based model. For the regions of interest (ROIs) in both the palm and finger, the average similarity scores were 17.85%. We present the Frechet Inception Distance (FID) of the proposed model, which achieved a 16.1 score, demonstrating significant performance. These results demonstrated StyleGAN as effective in producing unique synthetic biometric images. Full article

Palmprint recognition (PPR) has recently garnered attention due to its robustness and accuracy. Many PPR methods rely on preprocessing the region of interest (ROI). However, the emergence of ROI attacks capable of generating synthetic ROI images poses a significant threat to PPR systems. Despite this, ROI attacks are less practical since PPR systems typically take hand images as input rather than just the ROI. Therefore, there is a pressing need for a method that specifically targets the system by composing hand images. The intuitive approach involves embedding an ROI into a hand image, a comparatively simpler process requiring less data than generating entirely synthetic images. However, embedding faces challenges, as the composited hand image must maintain a consistent color and texture. To overcome these challenges, we propose a training-free, end-to-end hand image composition method incorporating ROI harmonization and palm blending. The ROI harmonization process iteratively adjusts the ROI to seamlessly integrate with the hand using a modified style transfer method. Simultaneously, palm blending employs a pretrained inpainting model to composite a hand image with a continuous transition. Our results demonstrate that the proposed method achieves a high attack performance on the IITD and Tongji datasets, with the composited hand images exhibiting realistic visual quality. Full article

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. Full article

The scientific study of privacy-preserving biometrics, represented by the palmprint, face, and iris, has grown tremendously. That being said, there has not been much attention paid to the proper preservation, transmission, and authentication of biometric images used in everyday applications. In this paper, we propose a new complete model for encrypting and decrypting biometric images, including their signing and authentication, using a nested algorithm of 3D Arnold Transform. In addition, the ElGamal Encryption Algorithm for the encryption part and the ElGamal Digital Signature for the signature part are applied. The model is mainly based on the Arnold Transform and Public-Key Cryptosystem, which are convenient for key transfer and fully functional. Here, the model succeeds in encrypting and securing the authentication process for privacy-preserving biometric images. Various tests have been carried out to demonstrate the feasibility and security of the proposed model and have been compared with existing encryption methods to achieve better results. Moreover, the proposed model can also be extended to the storage, transmission, and authentication of biometric data for daily use. Full article

The outputs of deep hash network (DHN) are binary codes, so DHN has high retrieval efficiency in matching phase and can be used for high-speed palmprint recognition, which is a promising biometric modality. In this paper, the templates and network parameters are both quantized for fast and light-weight palmprint recognition. The parameters of DHN are binarized to compress the network weight and accelerate the speed. To avoid accuracy degradation caused by quantization, mutual information is leveraged to optimize the ambiguity in Hamming space to obtain a tri-valued hash code as a palmprint template. Kleene Logic’s tri-valued Hamming distance measures the dissimilarity between palmprint templates. The ablation experiments are tested on the binarization of the network parameter, and the normalization and trivialization of the deep hash output value. The sufficient experiments conducted on several contact and contactless palmprint datasets confirm the multiple advantages of our method. Full article

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. Full article

Palmprint recognition has been widely applied to security authentication due to its rich characteristics, i.e., local direction, wrinkle, and texture. However, different types of palmprint images captured from different application scenarios usually contain a variety of dominant features. Specifically, the palmprint recognition performance will be degraded by the interference factors, i.e., noise, rotations, and shadows, while palmprint images are acquired in the open-set environments. Seeking to handle the long-standing interference information in the images, multiview palmprint feature learning has been proposed to enhance the feature expression by exploiting multiple characteristics from diverse views. In this paper, we first introduced six types of palmprint representation methods published from 2004 to 2022, which described the characteristics of palmprints from a single view. Afterward, a number of multiview-learning-based palmprint recognition methods (2004–2022) were listed, which discussed how to achieve better recognition performances by adopting different complementary types of features from multiple views. To date, there is no work to summarize the multiview fusion for different types of palmprint features. In this paper, the aims, frameworks, and related methods of multiview palmprint representation will be summarized in detail. Full article

Being an efficient image reconstruction and recognition algorithm, two-dimensional PCA (2DPCA) has an obvious disadvantage in that it treats the rows and columns of images unequally. To exploit the other lateral information of images, alternative 2DPCA (A2DPCA) and a series of bilateral 2DPCA algorithms have been proposed. This paper proposes a new algorithm named direct bilateral 2DPCA (DB2DPCA) by fusing bilateral information from images directly—that is, we concatenate the projection results of 2DPCA and A2DPCA together as the projection result of DB2DPCA and we average between the reconstruction results of 2DPCA and A2DPCA as the reconstruction result of DB2DPCA. The relationships between DB2DPCA and related algorithms are discussed under some extreme conditions when images are reshaped. To test the proposed algorithm, we conduct experiments of image reconstruction and recognition on two face databases, a handwritten character database and a palmprint database. The performances of different algorithms are evaluated by reconstruction errors and classification accuracies. Experimental results show that DB2DPCA generally outperforms competing algorithms both in image reconstruction and recognition. Additional experiments on reordered and reshaped databases further demonstrate the superiority of the proposed algorithm. In conclusion, DB2DPCA is a rather simple but highly effective algorithm for image reconstruction and recognition. Full article

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. Full article

The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples. However, the accuracy of the traditional principal component analysis method in dimension reduction is not very high, and it is very sensitive to outliers. In order to improve the robustness of image recognition to noise and the importance of geometric information in a given data space, this paper proposes a new unsupervised feature extraction model based on $l_{2,p}$-norm PCA and manifold learning method. To improve robustness, the model method adopts $l_{2,p}$-norm to reconstruct the distance measure between the error and the original input data. When the image is occluded, the projection direction will not significantly deviate from the expected solution of the model, which can minimize the reconstruction error of the data and improve the recognition accuracy. To verify whether the algorithm proposed by the method is robust, the data sets used in this experiment include ORL database, Yale database, FERET database, and PolyU palmprint database. In the experiments of these four databases, the recognition rate of the proposed method is higher than that of other methods when $p=0.5$. Finally, the experimental results show that the method proposed in this paper is robust and effective. Full article

Vein recognition has been drawing more attention recently because it is highly secure and reliable for practical biometric applications. However, underlying issues such as uneven illumination, low contrast, and sparse patterns with high inter-class similarities make the traditional vein recognition systems based on hand-engineered features unreliable. Recent successes of convolutional neural networks (CNNs) for large-scale image recognition tasks motivate us to replace the traditional hand-engineered features with the superior CNN to design a robust and discriminative vein recognition system. To address the difficulty of direct training or fine-tuning of a CNN with existing small-scale vein databases, a new knowledge transfer approach is formulated using pre-trained CNN models together with a training dataset (e.g., ImageNet) as a robust descriptor generation machine. With the generated deep residual descriptors, a very discriminative model, namely deep residual vector encoding (DRVE), is proposed by a hierarchical design of dictionary learning, coding, and classifier training procedures. Rigorous experiments are conducted with a high-quality hand-dorsa vein database, and superior recognition results compared with state-of-the-art models fully demonstrate the effectiveness of the proposed models. An additional experiment with the PolyU multispectral palmprint database is designed to illustrate the generalization ability. Full article

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. Full article