Biometric Modalities Research Articles

A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

Synthetic biometric samples are created with an ultimate goal of getting around privacy concerns, mitigating biases in biometric datasets, and reducing the sample acquisition effort to enable large-scale evaluations. The recent breakthrough in the development of neural generative models shifted the focus from image synthesis by mathematical modeling of biometric modalities to data-driven image generation. This paradigm shift on the one hand greatly improves the realism of synthetic biometric samples and therefore enables new use cases, but on the other hand new challenges and concerns arise. Despite their realism, synthetic samples have to be checked for appropriateness for the tasks they are intended which includes new quality metrics. Here, we highlight the benefits of using synthetic samples, review the use cases, and summarize and categorize the most prominent studies on synthetic biometrics aiming at showing recent progress and the direction of future research.

Biometric Modality Challenges and Future Prospects

Biometric Modality Challenges and Future Prospects

Vision Transformers for Vein Biometric Recognition

In October 2020, Google researchers present a promising Deep Learning architecture paradigm for Computer Vision that outperforms the already standard Convolutional Neural Networks (CNNs) on multiple image recognition state-of-the-art datasets: Vision Transformers (ViTs). Based on the self-attention concept inherited from Natural Language Processing (NLP), this new structure surpasses the CNN image classification task on ImageNet, CIFAR-100, and VTAB, among others, when it is fine-tuned (Transfer Leaning) after a previous pre-training on larger datasets. In this work, we confirm this theory and move one step further over the CNN structures applied for Vascular Biometric Recognition (VBR): to the best of our knowledge, we introduce for the first time multiple pure pre-trained and fine-tuned Vision Transformers in this evolving biometric modality to address the challenge of the limited number of samples in VBR datasets. For this purpose, the ViTs have been trained to extract unique image features on the ImageNet-1k and ImageNet-21k and then fine-tuned for the four main existing VBR variants, i.e., finger, palm, hand dorsal, and wrist vein areas. Fourteen existing vascular datasets have been used to perform the vein identification task in the four previously mentioned modalities, based on the True-Positive Identification Rate (TPIR) and 75-25% train-test sets obtaining the following results: HKPU (99.52%), and FV-USM (99.1%); Vera (99.39%), and CASIA (96.00%); Bosphorus (99.86%); PUT-wrist (99.67%), and UC3M-CV1+CV2 (99.67%). Furthermore, we introduce UC3M-CV3: a hygienic contactless wrist database collected on smartphones and consisting of 5400 images from 100 different subjects. The promising results show the Vision Transformer’s versatility in VBR under Transfer Learning and reinforce this new Neural Network architecture paradigm.

Human body odour as a biometric indicator for person identification

In biometric identification, various physiological and behavioral traits such as fingerprints, facial features, iris patterns, and voice have been extensively explored. This paper introduces human body odour as a biometric trait for person identification. Body odour, composed of volatile organic compounds unique to each individual, presents a challenge for biometric authentication. The study explores the scientific basis of human body odour, including its chemical composition and uniqueness. The paper presents the enrollment process for capturing and storing body odour data, emphasizing the potential of electronic nose (E-nose) devices for scent detection and pattern recognition. This paper discusses the advantages of body odour biometrics, including its resistance to masking by artificial scents and its potential to reduce password administration costs. The proposed odour biometric system offers a non-intrusive and reliable means of person identification, particularly in scenarios where traditional biometric modalities may be impractical or ineffective.

Thinning Algorithms Analysis Minutiae Extraction with Terminations and Bifurcation Extraction from the Single-Pixeled Thinned Biometric Image

Biometrics is the automatic identification of individuals based on physiological and behavioral characteristics. In today's technologically advanced digital world, it is regarded as the new digital key. There are two operating modes for biometric systems: identification and verification. The most popular biometric modalities are fingerprints, which are employed in many different industries and professions. Three distinct thinning methods are examined in this study. The proposed work looks into how thinning affects fingerprints, as well as minutiae extraction and texture feature analysis. To improve the quality of the fingerprints, thinning techniques such as Zhang- Suen's, Halls, and Guo Halls have been used. In terms of minutiae extraction, the thinning methods were compared. The minutiae points obtained were used to elaborate on the precision rate of fingerprints after processing. The simulations were run, and the experimental data was examined.

Gait Based Person Identification Using Deep Learning Model of Generative Adversarial Network

The proliferation of digital age security tools is often attributed to the rise of visual surveillance. Since an individual's gait is highly indicative of their identity, it is becoming an increasingly popular biometric modality for use in autonomous visual surveillance and monitoring. There are various steps used in gait recognition frameworks such as segmentation, feature extraction, feature learning and similarity measurement. These steps are mutually independent with each part fixed, which results in a suboptimal performance in a challenging condition. It can be done independently of the users' involvement. Low-resolution video and straightforward instrumentation can verify an individual's identity, making impersonation a rarity. Using the benefits of the Generative Adversarial Network (GAN), this investigation tackles the problem of unevenly distributed unlabeled data with infrequently performed tasks. To estimate the data circulation in various circumstances using constrained observed gait data, a multimodal generator is applied here. When it comes to sharing knowledge, the variety provided by the data generated by a multimodal generator is hard to beat. The capability to distinguish gait activities with varying patterns due to environmental dynamics is enhanced by this multimodal generator. This system is more stable than other gait-based recognition methods because it can process data that is not equally dispersed throughout a different environment. The system's reliability is enhanced by the multimodal generator's capacity to produce a wide variety of outputs. The testing results show that this algorithm is superior to other gait-based recognition methods because it can adapt to changing environments.

Periocular biometrics and its relevance to partially masked faces: A survey

Periocular is one of the promising biometric traits for human recognition. It encompasses a surrounding area of eyes that includes eyebrows, eyelids, eyelashes, eye-folds, eyebrows, eye shape, and skin texture. Its relevance is more emphasize during the COVID-19 pandemic due to the masked faces. So, this article presents a detailed review of periocular biometrics to understand its current state. The paper first discusses the various face and periocular techniques, specially designed to recognize humans wearing a face mask. Then, different aspects of periocular biometrics are reviewed: (a) the anatomical cues present in the periocular region useful for recognition, (b) the various feature extraction and matching techniques developed, (c) recognition across different spectra, (d) fusion with other biometric modalities (face or iris), (e) recognition on mobile devices, (f) its usefulness in other applications, (g) periocular datasets, and (h) competitions organized for evaluating the efficacy of this biometric modality. Finally, various challenges and future directions in the field of periocular biometrics are presented.

Heartprint: A Dataset of Multisession ECG Signal with Long Interval Captured from Fingers for Biometric Recognition

The electrocardiogram (ECG) signal produced by the human heart is an emerging biometric modality that can play an important role in the future generation’s identity recognition with the support of machine learning techniques. One of the major obstacles in the progress of this modality is the lack of public datasets with a long interval between sessions of data acquisition to verify the uniqueness and permanence of the biometric signature of the heart of a subject. To address this issue, we put forward Heartprint, a large biometric database of multisession ECG signals comprising 1539 records captured from the fingers of 199 healthy subjects. The capturing time for each record was 15 s, and recordings were made in resting and reading conditions. They were collected in multiple sessions over ten years, and the average interval between first session (S1) and third session (S3L) was 1572.2 days. The dataset also covers several demographic classes such as genders, ethnicities, and age groups. The combination of raw ECG signals and demographic information turns the Heartprint dataset, which is made publicly available online, into a valuable resource for the development and evaluation of biometric recognition algorithms.

Feature level fusion framework for multimodal biometric system based on CCA with SVM classifier and cosine similarity measure

ABSTRACT The multimodal biometric system using feature level fusion offers more accurate and reliable recognition performance than the unimodal system. But in practice, feature level fusion is challenging to perform when biometric modalities have heterogeneous and incompatible feature representation and enforce the final decision more confidently. One of the main concerns in the fusion of features is to drive the highly discriminatory representation amongst different biometric modalities. This paper aims to design a framework for an efficient feature level fusion based on canonical correlation analysis (CCA) with a support vector machine (SVM) classifier to get a highly discriminant and affine invariant fused feature vector. The principal component analysis (PCA) + CCA subspace approach is used to achieve dimensionality reduction and feature fusion in a coherent manner, This approach eliminates the need for a complex matcher/classifier design to process a fused feature vector and also reduces computational complexity. The experimental findings for the SDUMLA-HMT multimodal database demonstrate that CCA on the extracted feature sets of iris and fingerprint modalities results in reasonably better multimodal classification accuracy with a substantial reduction in the feature dimensions. Using SVM, we achieved a classification accuracy of 100%. In this paper furthermore, three different distance measures are explored to test the efficacy of the proposed CCA-based feature level fusion approach. The best recognition performance is achieved in terms of an equal error rate (EER) of 0.176% for the cosine similarity measure. We also compared the proposed approach with the match score level fusion method. The proposed feature level fusion approach excels the recognition performance in contrast to the other literature approaches.

A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features

Biometric verification is generally considered a one-to-one matching task. In contrast, in this paper, we argue that the one-to-many competitive matching via sparse representation-based classification (SRC) can bring enhanced verification security and accuracy. SRC-based verification introduces non-target subjects to construct dynamic dictionary together with the client claimed and encodes the submitted feature. Owing to the sparsity constraint, a client can only be accepted when it defeats almost all non-target classes and wins a convincing sparsity-based matching score. This will make the verification more secure than those using one-to-one matching. However, intense competition may also lead to extremely inferior genuine scores when data degeneration occurs. Motivated by the latent benefits and concerns, we study SRC-based verification using two sparsity-based matching measures, three biometric modalities (i.e., face, palmprint, and ear) and their multimodal combinations based on both handcrafted and deep learning features. We finally approach a comprehensive study of SRC-based verification, including its methodology, characteristics, merits, challenges and the directions to resolve. Extensive experimental results demonstrate the superiority of SRC-based verification, especially when using multimodal fusion and advanced deep learning features. The concerns about its efficiency in large-scale user applications can be readily solved using a simple dictionary shrinkage strategy based on cluster analysis and random selection of non-target subjects.

Convolutional Neural Network for Closed-Set Identification from Resting State Electroencephalography

In line with current developments, biometrics is becoming an important technology that enables safer identification of individuals and more secure access to sensitive information and assets. Researchers have recently started exploring electroencephalography (EEG) as a biometric modality thanks to the uniqueness of EEG signals. A new architecture for a convolutional neural network (CNN) that uses EEG signals is suggested in this paper for biometric identification. A CNN does not need complex signal pre-processing, feature extraction, and feature selection stages. The EEG datasets utilized in this research are the resting state eyes open (REO) and the resting state eyes closed (REC) EEG. Extensive experiments were performed to design this deep CNN architecture. These experiments showed that a CNN architecture with eleven layers (eight convolutional layers, one average pooling layer, and two fully connected layers) with an Adam optimizer resulted in the highest accuracy. The CNN architecture proposed here was compared to existing models for biometrics using the same dataset. The results show that the proposed method outperforms the other task-free paradigm CNN biometric identification models, with an identification accuracy of 98.54%.

A New Design of Iris Recognition Using Hough Transform with K-Means Clustering and Enhanced Faster R-CNN

Iris recognition method is the most significant biometric modality for human identification. Currently, multiple deep structured architectures have been employed in biometric recognition with various features like robust generalization ability, high accuracy, and automatic learning. It is also considered as a more emerging area in biometrics along with machine learning approaches. Deep CNN is a conventional approach of image processing broadly utilized in several fields, however, it is simply influenced by slight disturbances and also includes bad anti-noise capacity in image classification. The major scope of this work is to implement adaptive deep learning-aided iris recognition. The key contribution of this paper relies on enhancing iris segmentation and iris recognition. Initially, the iris images are pre-processed, which are subjected to segmentation. The segmentation approach focuses on a Hough transform with K-Means Clustering (HT-KMC). Once the segmentation of the iris is done, the “Enhanced Faster Region-Convolutional Neural Network (E-Faster-RCNN)” is developed for the recognition purpose. The number of suitably hidden neurons of the conventional Faster-RCNN is optimized by the Adaptive-Sail Fish Optimization (ASFO) algorithm to obtain the highest efficiency. The simulation findings of the recommended approach proved that the method is effective in iris recognition by experimenting on a benchmark dataset.

Age Prediction from Sclera Images using Deep Learning

Automatic age classification has drawn the interest of many scholars in the fields of machine learning and deep learning. In this study, we looked at the problem of estimating age groups using different biometric modalities of human beings. We looked at the problem of determining age groups in humans using various biometric modalities. Specifically, we focused on the use of transfer learning for sclera age group classification. 2000 Sclera images were collected from 250 individuals of various ages, and Otsu thresholding was used to segment the images using morphological processes. Experiment was conducted to determine how accurately the age group of a person can be classified from sclera images using pretrained CNN architectures. The segmented images were trained and tested on four different pre-trained models (VGG16, ResNet50, MobileNetV2, EffcientNet-B1), which were compared based on different performance metrics in which ResNet-50 was shown to outperform the others, resulting in an accuracy, precision, recall and F1-score of 95% while VGG-16, EffcientNetB1, and MobileNetV2 had 94%, 93%, and 91%, respectively. The findings from the study showed that there is an aging template in the sclera that can be utilized to classify age.

Multi-Biometric Iris Recognition System Using Consensus Between Convolutional Neural Network Architectures

The field of biometrics represents an important domain and is currently undergoing extensive research to improve data security. Among the most used biometric modalities, we find the iris which, is specific and adequate. It is unique for each human being. Iris identification is used at international airports, sea, and land borders. In this article, we propose to use convolutional neural networks (CNN) with a consensus between the architectures (VGG, DenseNet, Resnet, Inception, MobileNet, and Xception). The goal is to classify and combine CNN architectures with the Softmax classifier focusing on consensus orientation using a voting system (Hard voting). In addition, the use of optimization techniques such as Transfer learning, Batch-normalization, Dropout, Data augmentation, Global average pooling, and Cross-validation will accelerate the learning process and avoid overfitting. Using the GPU with its parallelization will significantly speed up the processing of our program. The experiments of our approach were carried out on the MMU1 iris database with an accuracy reaching 100%.

Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey.

Cognitive biometrics is an emerging branch of biometric technology. Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition. There is a major need to summarize the latest developments in this field. Existing surveys have mainly focused on a small subset of cognitive biometric modalities, such as EEG and ECG. This article provides a comprehensive review of cognitive biometrics, covering all the major biosignal modalities and applications. A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition. We provide a unified view of the methodological advances in these four aspects across various biosignals and applications, facilitating interdisciplinary research and knowledge transfer across fields. Furthermore, this article discusses open research directions in cognitive biometrics and proposes future prospects for developing reliable and secure cognitive biometric systems.

Thermal-Visible Face Recognition Based on CNN Features and Triple Triplet Configuration for On-the-Move Identity Verification.

Face recognition operating in visible domains exists in many aspects of our lives, while the remaining parts of the spectrum including near and thermal infrared are not sufficiently explored. Thermal–visible face recognition is a promising biometric modality that combines affordable technology and high imaging qualities in the visible domain with low-light capabilities of thermal infrared. In this work, we present the results of our study in the field of thermal–visible face verification using four different algorithm architectures tested using several publicly available databases. The study covers Siamese, Triplet, and Verification Through Identification methods in various configurations. As a result, we propose a triple triplet face verification method that combines three CNNs being used in each of the triplet branches. The triple triplet method outperforms other reference methods and achieves TAR @FAR 1% values up to 90.61%.

Sclera biometrics in restricted and unrestricted environment with cross dataset evaluation

Research on ocular biometric systems in unrestricted environment has grown steadily over the last decade. Though sclera recognition is widely investigated as an alternative or supplementary ocular biometric modality, most of the work has been done in restricted environment. So there are several unanswered questions like, how a model trained in a restricted environment performs in an unrestricted scenario, or whether the performance improves if it is trained in a combined environment. In this work, we aim to study sclera biometrics in multiple environments and answer these questions. In the process, we have worked on all components in the sclera biometric pipeline, viz., sclera segmentation, vessel extraction, gaze detection and sclera recognition. We have trained and tested the deep models proposed for the tasks with high-quality datasets prepared in restricted environment as well as mobile datasets prepared in unrestricted environment. We have also trained the models with images from two or more datasets. This helps us to ascertain how the images captured in diverse environments can be used collectively for the training purpose. We have used two high-quality datasets SBVPI, MASD and two mobile datasets MSD, MASDUM for our work. MASDUM is the proposed mobile dataset prepared in unrestricted environment. It is designed specifically with research of sclera biometrics in mind. It contains a total of 1055 eye images along with 1055 sclera-segmented and 68 sclera-vessel-mark-up images. Since there is a severe lack of vessel-annotated datasets in the field, MASDUM dataset hopefully caters the need of researchers. State-of-the-art results are achieved for sclera segmentation on the datasets SBVPI, MASD, MSD and MASDUM with F1-scores of 96.3, 97.3, 87.9 and 91.2 respectively. State-of-the-art results are also achieved for sclera recognition with respective EERs of 0.132, 0.079, 0.086 and 0.080. We also present in-depth analysis on the novel idea of Dual-output variant of recognition model DeepR. This variant directly produces the matching score between two vessel-extracted binary images and it shows better results by largely reducing FAR.

Iris Liveness Detection Using Multiple Deep Convolution Networks

In the recent decade, comprehensive research has been carried out in terms of promising biometrics modalities regarding humans’ physical features for person recognition. This work focuses on iris characteristics and traits for person identification and iris liveness detection. This study used five pre-trained networks, including VGG-16, Inceptionv3, Resnet50, Densenet121, and EfficientNetB7, to recognize iris liveness using transfer learning techniques. These models are compared using three state-of-the-art biometric databases: the LivDet-Iris 2015 dataset, IIITD contact dataset, and ND Iris3D 2020 dataset. Validation accuracy, loss, precision, recall, and f1-score, APCER (attack presentation classification error rate), NPCER (normal presentation classification error rate), and ACER (average classification error rate) were used to evaluate the performance of all pre-trained models. According to the observational data, these models have a considerable ability to transfer their experience to the field of iris recognition and to recognize the nanostructures within the iris region. Using the ND Iris 3D 2020 dataset, the EfficeintNetB7 model has achieved 99.97% identification accuracy. Experiments show that pre-trained models outperform other current iris biometrics variants.

Convolutional neural network with improved feature ranking for robust multi-modal biometric system

In recent years biometric identification and verification of human beings based on their physiological or behavioral characteristics have become a trend in security applications. The existing multi-modal biometric system (MBS) uses machine learning models for multi class classification, but they fail in providing maximum confidentiality, security, and accuracy. The proposed work adopts a Deep Learning Convolutional Neural Network (DLCNN) approach to implement the robust MBS. The proposed system is well trained and tested using four different datasets, including biometric images of the face, ear, palm, and finger. Initially, noise from the datasets is eliminated using Gabor filtering. Feature extraction and dimensionality reduction operations are implemented by using the Improved Principal Component Analysis (IPCA) technique. Here, IPCA is used for the fusion of four unique biometric features with the matching score, and feature ranking is performed using the Extended Borda-Count method. Further, DLCNN-based classification is used for the recognition of all biometric modalities, which improves the robustness and accuracy of the system. The proposed DLCNN-Gabor based IPCA technique improves the biometric recognition rate compared to existing methods.

Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier

Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger–knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10.