Finger Vein Images Research Articles

Finger Vein Recognition Based on Anatomical Features of Vein Patterns

Finger vein recognition is a promising biometric authentication technique that depends on the unique features of vein patterns in the finger for recognition. The existing finger vein recognition methods are based on minutiae features or binary features such as LBP, LLBP, PBBM etc. or from the entire vein pattern. However, the minutiae-based features cannot accurately represent the structural or anatomical aspects of the vein pattern. These issues with the minutia feature led to increased false matches. Recognition based on binary features have limitations such as increased false matches, sensitivity to the translation and rotation, security and privacy issues etc. A feature representation based on the anatomy of vein patterns can be an alternative solution to improve the recognition performance. In the IJCB 2020 conference, we showed that every finger vein image contains one or more of a kind of 4 special vein patterns which we refereed as Fork, Eye, Bridge, and Arch (FEBA). In this paper, we further enlarge this set to 6 vein patterns (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> EB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> A) by identifying two variations in the Fork and Bridge vein patterns. Based on 6 anatomical features of the possible 6 vein patterns in a vein image, we define a 6 × 6 feature matrix representation for finger vein images. Since this feature representation is based on the anatomical properties of the local vein patterns, it provides template security. Further we show that, the proposed feature representation is invariant to scaling, translation, and rotation changes. The experimental results using two open datasets and an in-house dataset show that the proposed method has a better recognition performance when compared to the existing approaches with an EER around 0.02% and an average recognition accuracy of 98%.

Finger vein recognition method based on ant colony optimization and improved EfficientNetV2.

Deep learning is an important technology in the field of image recognition. Finger vein recognition based on deep learning is one of the research hotspots in the field of image recognition and has attracted a lot of attention. Among them, CNN is the most core part, which can be trained to get a model that can extract finger vein image features. In the existing research, some studies have used methods such as combination of multiple CNN models and joint loss function to improve the accuracy and robustness of finger vein recognition. However, in practical applications, finger vein recognition still faces some challenges, such as how to solve the interference and noise in finger vein images, how to improve the robustness of the model, and how to solve the cross-domain problem. In this paper, we propose a finger vein recognition method based on ant colony optimization and improved EfficientNetV2, using ACO to participate in ROI extraction, fusing dual attention fusion network (DANet) with EfficientNetV2, and conducting experiments on two publicly available databases, and the results show that the recognition rate using the proposed method on the FV-USM dataset reaches The results show that the proposed method achieves a recognition rate of 98.96% on the FV-USM dataset, which is better than other algorithmic models, proving that the method has good recognition rate and application prospects for finger vein recognition.

FVFSNet: Frequency-Spatial Coupling Network for Finger Vein Authentication

Finger vein biometrics is becoming an important source of human authentication due to its advantages in terms of liveness detection, high security, and user convenience. Although there exist a lot of deep learning-based methods for finger vein authentication, they only extract features from finger vein images in the spatial domain and may lose some important information that is present in other domains, such as the frequency domain. Motivated by this conjecture and the remarkable performance of image feature extraction in the frequency domain, this work explores a method capable of extracting finger vein features in both the spatial and frequency domains. Therefore, the features extracted from different domains can complement each other. In addition, we propose a novel frequency-spatial coupling network (FVFSNet) for finger vein authentication. FVFSNet is mainly composed of three parts: (1) the frequency domain processing module (FDPM), (2) the spatial domain processing module (SDPM), and (3) the frequency-spatial coupling module (FSCM). The FDPM is used to extract the finger vein features present in the frequency domain, which is mainly composed of the frequency-spatial domain transformation and the frequency domain convolution layer. The SDPM is used to extract the finger vein features present in the spatial domain, which is mainly composed of convolution layers with an efficient design. The FSCM is used to couple the features extracted from the FDPM and SDPM, which is mainly composed of the channel and spatial attention mechanisms. To validate our conjecture and the performances of FVFSNet, extensive experiments are conducted on nine commonly used publicly available finger vein datasets. Experimental results show that the frequency domain constitutional neural network has a surprising effect on finger vein authentication, and the proposed FVFSNet achieves the state-of-the-art performance with the advantages of lightweight and low computational cost.

An Attention-Based Deep Regional Learning Model for Enhanced Finger Vein Identification

Finger vein biometrics is one of the most promising ways to identify a person because it can provide uniqueness, protection against forgery, and bioassay. Due to the limitations of the imaging environments, however, the finger vein images that are taken can quickly become low-contrast, blurry, and very noisy. Therefore, more robust and relevant feature extraction from the finger vein images is still open research that should be addressed. In this paper, we propose a new technique of deep learning that is based on the attention mechanisms for human finger vein image identification and recognition and is called deep regional learning. Our proposed model relies on an unsupervised learning method that depends on optimized K-Means clustering for localized finger vein mask generation. The generated binary mask is used to build our attention learning model by making the deep learning structure focus on the region-of-interest (ROI) learning instead of learning the whole feature domain. This technique makes the Deep Regional Attention Model learn more significant features with less time and computational resources than the regular deep learning model. For experimental validation, we used different finger vein imaging datasets that have been extracted and generated using our model. Original finger vein images, localized finger vein images (with no background), localized grayscale finger vein images (grayscale images with no background and projected finger vein lines), and localized colored finger vein images (colored images with no background and projected finger vein lines) are used to train and test our model, which gets better results than traditional deep learning and other methods.

A study on finger vein recognition with few samples based on residual connected meta‐learning

AbstractA meta‐learning based method for finger vein recognition with few samples is proposed to overcome the problem of low recognition accuracy caused by the small number and variety of finger vein samples as well as fuzzy vein lines. The method is based on meta‐learning, incorporating multiscale features, and using the idea of residual networks to join meta‐learning to improve the recognition accuracy of finger vein images with few samples; to further improve its recognition ability, a differential map is constructed in the form of a differential between the finger vein image of singular value decomposition and finger vein image. We are the first to apply meta‐learning to the field of finger vein recognition, to our knowledge, and the experiments show that this approach is superior, with recognition accuracy of up to 99.13% for finger vein datasets with few‐shot samples.

Driver Identification System Using Finger Vein and YOLO Object Detection

Biometrics used to identify people is one of the safest and most convenient identification methods. The hypoxic hemoglobin method can present a human finger vein image for driver identification through infrared ray irradiation. The finger vein image is captured system; with the finger vein image processed using contrast-limited adaptive histogram equalization (CLAHE) and the Gabor filter to obtain a clearer image. The YOLO object detection technology is used to drive identification. The proposed system can be divided into two parts. The first part is the training part, which processes images. A database generates the weight files for external testing. The second part is the testing system. When the driver's finger is placed in the designated photo area a photo will be taken. The photo will be processed and run on Raspberry Pi 4 together with the weight file to identify the driver. The experimental results show that when the database has a large amount of data, the yolov4-tiny-hy recognition rate is comparable to that of YOLOv4. The training time is greatly shortened.

The neglected background cues can facilitate finger vein recognition

Recently, finger vein based biometric authentication has attracted considerable attention due to its high efficiency and high security. However, most existing finger vein representation methods focus on vein traits while ignoring background cues, although background cues also convey identity information specific to each individual. In this paper, we leverage background intensity variations in finger vein images as new features to enrich discriminative representation, and accordingly propose a new descriptor named Intensity Orientation Vector (IOV). IOV, scaleable to reflect characteristics of finger tissues, offers additional informative cues for finger vein representation. Furthermore, we propose a new learning scheme named Semantic Similarity Preserved Discrete Binary Feature Learning (SSP-DBFL) for finger vein recognition. Unlike the most bimodal binary feature representation methods, SSP-DBFL preserves high-level semantic similarity in a common Hamming space to exploit the consensus between vein traits and background cues. Specifically, given a finger vein image, we first extract the direction difference vectors (DDV) as the main vein traits and the IOV as the auxiliary background cues. Subsequently, we jointly learn projection functions from these two types of features in a supervised manner, converting the two features into discriminative binary codes with their semantic similarity preserved. Finally, the binary codes are pooled into histogram-based vectors for finger vein representation. Extensive experiments are conducted on five widely used finger vein databases and demonstrate the effectiveness of our proposed IOV and SSP-DBFL.

Exploiting Multiperspective Driven Hierarchical Content-Aware Network for Finger Vein Verification

The finger vein trait has attracted widespread attention for personal authentication in recent years. However, most finger vein verification methods are performed on the single perspective, captured by a monocular near-infrared camera fixed at one side of the finger. Consequently, the contents of a single perspective have few details of the spatial network structure of the finger vein and show noticeable differences even if the posture of the same finger is slightly different. Both of them impact the verification performance. Hence, finger vein images captured from different viewpoints are considered in this work. We first design a low-cost multi-perspective based dorsal finger vein imaging device for data collection. A deep neural network named Hierarchical Content-Aware Network (HCAN) is then proposed to extract the discriminative hierarchical features of the finger vein. Specifically, HCAN is compound of a Global Stem Network (GSN) and a Local Perception Module (LPM). GSN aims to extract the latent global 3D feature from all perspectives through a recurrent neural network. It enables the model to retain the details in previous hidden states by incorporating a memory weighting strategy. LPM is designed to perceive each perspective from the aspect of image entropy. Guided by the entropy loss, LPM captures the prominent local feature and improves the discriminability and robustness of the hierarchical feature. The experimental results on the newly collected THU-MFV database demonstrate the superiority of the proposed method in comparison with other multi-perspective and single-perspective based methods.

RMOBF-Net: Network for the Restoration of Motion and Optical Blurred Finger-Vein Images for Improving Recognition Accuracy

Biometrics is a method of recognizing a person based on one or more unique physical and behavioral characteristics. Since each person has a different structure and shape, it is highly secure and more convenient than the existing security system. Among various biometric authentication methods, finger-vein recognition has advantages in that it is difficult to forge because a finger-vein exists inside one’s finger and high user convenience because it uses a non-invasive device. However, motion and optical blur may occur for some reasons such as finger movement and camera defocusing during finger-vein recognition, and such blurring occurrences may increase finger-vein recognition error. However, there has been no research on finger-vein recognition considering both motion and optical blur. Therefore, in this study, we propose a new method for increasing finger-vein recognition accuracy based on a network for the restoration of motion and optical blurring in a finger-vein image (RMOBF-Net). Our proposed network continuously maintains features that can be utilized during motion and optical blur restoration by actively using residual blocks and feature concatenation. Also, the architecture RMOBF-Net is optimized to the finger-vein image domain. Experimental results are based on two open datasets, the Shandong University homologous multi-modal traits finger-vein database and the Hong Kong Polytechnic University finger-image database version 1, from which equal error rates of finger-vein recognition accuracy of 4.290–5.779% and 2.465–6.663% were obtained, respectively. Higher performance was obtained from the proposed method compared with that of state-of-the-art methods.

ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition

Finger vein recognition has been widely studied due to its advantages, such as high security, convenience, and living body recognition. At present, the performance of the most advanced finger vein recognition methods largely depends on the quality of finger vein images. However, when collecting finger vein images, due to the possible deviation of finger position, ambient lighting and other factors, the quality of the captured images is often relatively low, which directly affects the performance of finger vein recognition. In this study, we proposed a new model for finger vein recognition that combined the vision transformer architecture with the capsule network (ViT-Cap). The model can explore finger vein image information based on global and local attention and selectively focus on the important finger vein feature information. First, we split-finger vein images into patches and then linearly embedded each of the patches. Second, the resulting vector sequence was fed into a transformer encoder to extract the finger vein features. Third, the feature vectors generated by the vision transformer module were fed into the capsule module for further training. We tested the proposed method on four publicly available finger vein databases. Experimental results showed that the average recognition accuracy of the algorithm based on the proposed model was above 96%, which was better than the original vision transformer, capsule network, and other advanced finger vein recognition algorithms. Moreover, the equal error rate (EER) of our model achieved state-of-the-art performance, especially reaching less than 0.3% under the test of FV-USM datasets which proved the effectiveness and reliability of the proposed model in finger vein recognition.

A Novel System and Experimental Study for 3D Finger Multibiometrics

Fingers contain various discriminative biometric traits, such as fingerprint, finger vein, finger knuckle, finger shape, and so on, which are complementary in identity information. However, only one or a few traits are used in most current research and practical applications, while others are ignored, resulting in degraded recognition performance and vulnerability to forgery. In this paper, we make the first attempt to collect and study all biometric traits on the finger. Firstly, a novel multi-view, multi-spectral 3D finger imaging system is proposed. To the best of our knowledge, it is the first biometric imaging system capable of capturing almost all finger-based traits. We scanned numerous fingers with this 3D finger imaging system, obtaining external skin images and internal vein images from 6 different views. The proposed 3D finger reconstruction and texture mapping algorithms are then used to generate 3D finger models with skin and vein textures. Second, we establish a benchmark dataset, namely the Large-scale Finger Multi-Biometric database and benchmark for 3D Finger Biometrics (LFMB-3DFB). The LFMB-3DFB contains 695 fingers, and each finger is acquired 10 times, yielding 6 finger skin images and 6 finger vein images for a total of 83,400 images and 6,950 3D finger models. Finally, we design a more scientific and comprehensive evaluation protocol to conduct extensive experimental research and analysis on this database for both subject-independent verification and subject-independent close-set identification tasks. Comprehensive and rigorous experiments for 2D finger traits recognition, multi-view finger traits recognition, 3D finger traits recognition, and score-level fusion on LFMB-3DFB are carried out, and excellent results are achieved. The LFMB-3DFB database will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SCUT-BIP-Lab/LFMB-3DFB</uri> to promote 3D finger multi-biometric research using cutting-edge imaging techniques.

Spoof detection based on score fusion using ensemble networks robust against adversarial attacks of fake finger-vein images

Finger-vein recognition is a biometric technology, which makes use of the pattern of veins in the skin of fingers. Finger-vein recognition is widely employed owing to its high recognition rate and ease of use. A spoof detection method is essential for identifying spoof attacks in biometric-recognition systems. Studies have primarily investigated methods for detecting spoof attacks that utilize image printing or replay using fake finger-vein images. However, no studies have investigated the detection of spoof attacks based on fake finger-vein images generated through a generative adversarial network (GAN), which is commonly implemented in deep-learning technologies. To solve this problem, we newly propose a spoof attack detection method robust against GAN-based fake finger-vein image generation. We verified the use of spoof finger-vein images generated through cycle-consistent adversarial networks (CycleGAN) to attack conventional finger-vein recognition systems and used this finding as the motivation for our research. We also propose a finger-vein spoof-detection method that employs score fusion of ensemble networks based on support vector machine using adaptive moment estimation (Adam) with a sharpness-aware minimization (SAM) optimizer. We conducted experiments using two open databases, ISPR and Idiap, to evaluate the proposed framework. The average classification error rates (ACER) of spoof detection using the proposed method with ISPR and Idiap were 0.37% and 0.23%, respectively. This result confirms the better performance by our method than conventional state-of-the-art methods.

Finger-Vein Image Dual Contrast Adjustment and Recognition Using 2D-CNN

The suggested process enhances the low contrast of the finger-vein image using dual contrast adaptive histogram equalization (DCLAHE) for visual attributes. The finger-vein histogram intensity is split out all over the image when dual CLAHE is used. For preprocessing, the finger-vein image dataset is obtained from the SDUMLA-HMT finger-vein database. Following the deployment of DCLAHE, the updated dataset is used to recognize objects using an improved 2D-CNN model. The 2D CNN model learns features by optimizing values of a preprocessed dataset. The accuracy of this model stands at 91.114%.

An ROI Extraction Method of Finger Vein Images Based on Large Receptive Field Gradient Operator for Accurate Localization of Joint Cavity.

Region of interest (ROI) extraction is a key step in finger vein recognition preprocessing. The current method takes the finger region in the vein image as the ROI, but this method cannot obtain better recognition accuracy because it only removes the background noise of the image and ignores factors such as the position and shape of the finger. To solve this problem, we limited the ROI to a fixed region between two finger joint cavities, proposed a new large receptive field gradient operator, and designed and implemented a new method for ROI extraction. It uses a large receptive field to search the target, which is similar to human vision, thus solving the problem of difficult ROI localization for images with large gradient areas. Moreover, for external factors such as noise and uneven illumination in the vein image, the interference factors can be eliminated by averaging them to a larger range with a larger size operator to improve the accuracy of the subsequent matching recognition. To verify the effectiveness of the proposed method, we conducted sufficient matching experiments on three public finger vein datasets. On various datasets, our method significantly reduced the identified EER value, with the lowest EER value reaching 0.96%. The experimental results show that the proposed ROI extraction method can effectively eliminate the influence of finger position, finger shape, and other factors on the subsequent recognition performance, accurately locate the finger joint cavity, and effectively improve the recognition performance.

A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections.

In the field of biometric recognition, finger vein recognition has received widespread attention by virtue of its advantages, such as biopsy, which is not easy to be stolen. However, due to the limitation of acquisition conditions such as noise and illumination, as well as the limitation of computational resources, the discriminative features are not comprehensive enough when performing finger vein image feature extraction. It will lead to such a result that the accuracy of image recognition cannot meet the needs of large numbers of users and high security. Therefore, this paper proposes a novel feature extraction method called principal component local preservation projections (PCLPP). It organically combines principal component analysis (PCA) and locality preserving projections (LPP) and constructs a projection matrix that preserves both the global and local features of the image, which will meet the urgent needs of large numbers of users and high security. In this paper, we apply the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger vein database to evaluate PCLPP and add “Salt and pepper” noise to the dataset to verify the robustness of PCLPP. The experimental results show that the image recognition rate after applying PCLPP is much better than the other two methods, PCA and LPP, for feature extraction.

ConvNet for Finger Vein based Personal Authentication

The human brain, can easily perceive and differentiate the objects in an image. Subsequently the field of computer vision intent to mimic / simulate the human vision system. Finger vein-based user authentication has been used to control access and maintaining privacy of confidential data. The main challenges in the finger vein verification are the quality of an acquired images due to uneven illumination of light, quality of sensor, positional variation and environmental condition. In this article, we used Wiener filter, to improve the quality of finger vein images. These noise free images are provided for training to popular pretrained ConvNet architecture for user verification using finger vein biometric. Then we analysed the performance of ConvNet (convolutional neural networks) such as Alex Net, Squeeze Net, Google Net, Shuffle Net, Efficient Net, Mobile Net, Res Net, Dense Net and NASNet for the finger vein based personal authentication to secure confidential data and maintain privacy. The finger vein images from Kaggle database is used for this research work. The experiment exhibits the outstanding performance of resnet101 with the 97.64% accuracy over its peer networks.

V-Vibe: A robust ROI extraction method based on background subtraction for vein images collected by infrared device

In recent years, the global epidemic has put forward higher requirements for public health security, and the non-contact finger vein acquisition and identification technology based on infrared imaging has become a research hotspot due to its high security, accuracy and convenience. However, infrared images generally have the problems of low resolution, low contrast and low signal-to-noise ratio, which seriously affect the effective extraction and utilization of image feature information. Moreover, most of the images captured by unconstrained vein acquisition devices have a large number of complex backgrounds, which are often similar to the pixel distribution of finger region, which greatly affects the recognition performance of vein images. To solve the above problems, we carried out the following exploratory work. First, we designed and developed a new finger vein infrared acquisition device. The device adopts a kind of physical structure of triangular spotlight, and in combination with our proposed V-Vibe method can achieve complete Region of Interest (ROI) extraction of finger vein images. Second, we created a new finger vein dataset (named FV-BS) for background subtraction. The dataset contains 420 individuals, 4,200 finger vein image samples and 420 device background images, as well as a subset of vein image data filled with complex backgrounds. Third, we proposed a new method named V-Vibe to extract ROI from vein images by using background subtraction technology. Subtraction can effectively eliminate factors that affect recognition performance in both vein images and background sample set, thus greatly reducing the influence of complex backgrounds and low-quality infrared images on identification. To verify the validity and accuracy of the V-Vibe method, we designed and conducted a large number of experiments on several datasets, including four publicly available datasets, as well as self-built FV-BS, and analyzed the results in detail. Compared with other methods, the ROI extraction effect of V-Vibe method proposed in this paper is significantly improved, and EER is reduced by 1.18% and 2.37% on average respectively in maximum curvature and repeated line tracking feature extraction methods, which is an effective method for ROI extraction of finger vein images. Furthermore, we also tested the proposed V-Vibe method to extract ROI from the vein images of palm and back of hands. Experimental results show that our method has excellent segmentation accuracy on both palm and dorsal hand vein datasets, with the average F1 Score reaching 95.1%.

A Simple and Efficient Method for Finger Vein Recognition.

Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishing ability, security, and non-invasive procedure. The main idea of traditional schemes is to directly extract features from finger vein images and then compare features to find the best match. However, the features extracted from images contain much redundant data, while the features extracted from patterns are greatly influenced by image segmentation methods. To tackle these problems, this paper proposes a new finger vein recognition algorithm by generating code. The proposed method does not require an image segmentation algorithm, is simple to calculate, and has a small amount of data. Firstly, the finger vein images were divided into blocks to calculate the mean value. Then, the centrosymmetric coding was performed using the matrix generated by blocking and averaging. The obtained codewords were concatenated as the feature codewords of the image. The similarity between vein codes is measured by the ratio of minimum Hamming distance to codeword length. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed method. The results indicate that our method outperforms the state-of-the-art methods and has competitive potential in performing the matching task.

Finger-vein presentation attack detection using depthwise separable convolution neural network

Biometrics is a powerful tool for identifying and authenticating persons based on their unique characteristics. Finger vein (FV) seems to be an emerging biometric of all types of hand-based biometrics, which have garnered considerable interest because of the extensive information and ease of implementation. As the FV system has grown in popularity, there have been numerous attempts to compromise it. Recent research reveals that the finger-vein recognition (FVR) system is vulnerable to presentation attacks, in which the sensory device accepts a fake printed FV image and grants access as if it were a genuine attempt. Few deep learning (DL) studies were developed in the past to detect spoof attacks in FV images. Existing works rely on performance improvement by neglecting the problems of the limited dataset, high computational complexity, and unavailability of lightweight and efficient feature descriptors. Therefore, we have proposed a novel depthwise separable convolution neural network (DSC) with residual connection and a linear support vector machine (LSVM) for the automatic detection of FV presentation attacks. At first, we apply the data augmentation method to raw images to increase the size of datasets. Afterward, this DSC technique is applied to extract robust features from FV images. Finally, a LSVM classifier is used to classify the images into bonafide and fake images. The proposed DSC-LSVM method is evaluated on two publicly available datasets such as Idiap and SCUT-FVD. The experimental results show that the DSC-SVM model attained a low error rate of 0.00% for presentation attack detection (PAD) on both datasets compared to state-of-the-art approaches. Several experimental results show that the DSC-LSVM model required less computation time and fewer parameters to detect spoof finger vein images. It concludes that the DSC-LSVM system is outperformed for PAD on finger vein images compared to existing methods.

Trilateral Filterative Hermitian feature transformed deep perceptive fuzzy neural network for finger vein verification

Automated person recognition is an essential field to preserve individual privacy in biometrics. A conventional system performs the recognition, but inaccurate detection of finger-vein lines causes lesser verification accuracy. To address this problem, a novel Trilateral Filterative Hermitian Feature Transformation based Deep Perceptive Fuzzy Neural Network (TFHFT-DPFNN) model is introduced for the capability of learning biometric features that offers robust and accurate vein image verification with minimum time. The proposed TFHFT-DPFNN model performs four different processing stages in the network layers. In the first stage, multiple finger vein images are captured from the database. Then the input images are taken to perform the denoising in the second stage by applying a guided trilateral filter to smooth the input image for accurate matching with minimum time consumption. Followed by, Hermitian Hat wavelet transformation is applied to perform decomposition and feature extraction from the input image. Then, the extracted features are transferred to the next hidden layer where the feature matching process is carried by applying the Jaccard similarity index. Based on the similarity value, the finger vein verification is performed through the fuzzy triangular membership function at the output layer. Finally, the verified results are obtained at the output layer with minimum error. Experimental evaluation is carried out with training images and testing images on different factors such as peak signal-to-noise ratio, finger vein verification accuracy, false-positive rate, and computation time with the number of finger vein images. The proposed TFHFT-DPFNN model offers promising outcomes in terms of higher verification accuracy and lesser false-positive rate with minimum time consumption than the conventional methods.