Biometric Recognition Methods Research Articles

A Biometric Identification for Multi-Modal Biomedical Signals in Geriatric Care

With the acceleration of global population aging, the elderly have an increasing demand for home care and nursing institutions, and the significance of health prevention and management of the elderly has become increasingly prominent. In this context, we propose a biometric recognition method for multi-modal biomedical signals. This article focuses on three key signals that can be picked up by wearable devices: ECG, PPG, and breath (RESP). The RESP signal is introduced into the existing two-mode signal identification for multi-mode identification. Firstly, the features of the signal in the time–frequency domain are extracted. To represent deep features in a low-dimensional feature space and expedite authentication tasks, PCA and LDA are employed for dimensionality reduction. MCCA is used for feature fusion, and SVM is used for identification. The accuracy and performance of the system were evaluated using both public data sets and self-collected data sets, with an accuracy of more than 99.5%. The experimental data fully show that this method significantly improves the accuracy of identity recognition. In the future, combined with the signal monitoring function of wearable devices, it can quickly identify individual elderly people with abnormal conditions, provide safer and more efficient medical services for the elderly, and relieve the pressure on medical resources.

Electrocardiogram identification based on data generative network and non-fiducial data processing

Nowadays, the use of biological signals as a criterion for identity recognition has gained increasing attention from various organizations and companies. Therefore, it has become crucial to have a biometric identity recognition method that is fast and accurate. In this paper, we propose a linear electrocardiogram (ECG) data preprocessing algorithm based on Kalman filters for rapid noise data filtering (wavelet transform filtering algorithm). Additionally, we introduce a generative network model called Data Generation Strategy Network (DRCN) based on generative networks. The DRCN is employed to augment training samples for convolutional classification networks, ultimately improving the classification performance of the model. Through the final experiments, our method successfully reduced the average misidentification rate of ECG-based identity recognition to 2.5%, and achieved an average recognition rate of 98.7% for each category, significantly surpassing previous achievements. In the future, this method is expected to be widely applied in the field of ECG-based identity recognition.

Facial Recognition Based Automated Door

To ensure the accuracy and efficiency of intruder identification, the proposed method is combined with Haar clssifier technology for face detection. When someone comes to the door, the Pi camera captures the image and starts the face detection process. In this research, we implement a facial recognition component to capture human images, comparing them with stored data in a database. Upon a match with an authorized individual, the system unlocks the door through an electromagnetic lock. The demand for a rapid and accurate face recognition system persists, continuously evolving to swiftly identify intruders and restrict unauthorized access to highly secure areas, thereby reducing human error. Facial recognition stands as a crucial component within secure systems, surpassing biometric pattern recognition methods, and finds widespread application across various domains.

A Multimodal Biometric Recognition Method Based on Federated Learning

Recently, multimodal authentication methods based on deep learning have been widely explored in biometrics. Nevertheless, the contradiction between the data privacy protection and the requirement of sufficient data when model optimizing has become increasingly prominent. To this end, we proposes a multimodal biometric federated learning framework (FedMB) to realize the multiparty joint training of identity authentication models with different modal data while protecting the users’ data privacy. Specifically, a personalized multimodal biometric recognition model fully trained by each participant is first obtained to improve the authentication performance, using modal point clustering with class‐first federated learning methods on the service side with the modal. Then a complementary multimodal biometric recognition strategy is implemented to build a complementary modal model. Finally, the fusion participant local model, with the modal model and complementary modal model, is trained by all participants again to obtain a more personalized modal model. The experimental results have demonstrated that the proposed FedMB can either protect the data privacy or utilize the data from all participants to train the personalized biometric recognition model to improve identity authentication performance.

Sclera Segmentation and Recognition for Spectacle Dataset

Sclera, a connective tissue enveloping the eye, emerges as a novel biometric recognition method for human identification. The composition of blood vessels in the sclera proves ideal for biometric use—visible with ease, stable over time, and unique to each individual. This paper proposes sclera segmentation and recognition techniques tailored for individuals wearing spectacles. The SSV (Spectacle Sclera Vision) Dataset was meticulously created to address challenges introduced by eyewear, including reflections, distortions, and illumination variations. The study explores the unique characteristics of the sclera region, presenting a comparative analysis of traditional and neural network-based segmentation and recognition methods on the SSV Dataset. Notably, Linear SVC outperforms CNN in recognition, and UNET demonstrates superior sclera segmentation compared to OTSU. The findings provide a foundation for potential advancements in developing robust multi-class classification models for sclera biometrics in real-world scenarios. Future work involves further analysis, scalability testing, and exploration of diverse applications in ocular health and security systems.

Survey Analysis on Secured user Authentication through Biometric Recognition

Secured user authentication is the process of verifying the user authenticity. Biometric authentication is the human identification system employed to match the biometric characteristics of user for verifying the authenticity. Biometric identifiers are exclusive, making it harder to hack accounts using them. Common types of biometrics comprise the fingerprint scanning verifies authentication based on a user's fingerprints Face recognition and voice recognition are employed in real-time application for improving the security level in different application scenarios. Face recognition is a method of identifying or verifying the individual identity using their face expression. Voice recognition is the ability of machine to receive and interpret the dictation to understand. Many researchers carried out their research on different face and voice recognition methods. But, recognition accuracy was not improved with minimum time consumption by existing biometric recognition method. In this research, different recognition methods are reviewed using biometric recognition method for user authentication. The recognition methods are efficiently on human faces dataset with respect to performance metrics like recognition accuracy, error rate, and recognition time.

Feasibility study on person identification utilizing frequency response functions of human fingers

A novel biometric recognition method is presented for future personal devices, utilizing the frequency responses of the human body, especially the fingers, for personal identification. A series of experimental vibration modal analyses were conducted to measure the frequency response functions (FRFs) of individuals’ fingers. In addition, a biodynamic lumped system model was constructed for the major components of a finger, including phalanges, joints, and skin. Analytical FRFs were then computed for a comparative study with the measured FRFs. In the person identification process, we employed an effective feature extraction method based on the correlation coefficient between frequency bins of the measured FRFs to extract the most effective set of frequency bins from the entire FRF spectrum. These extracted features were utilized to train a support vector machine for classifying individuals. In a controlled experimental setup, the classification results showed a maximum accuracy of 99%, affirming the feasibility of using the vibration responses of human fingers as a novel biometric method for person recognition.

Joint multi-type feature learning for multi-modality FKP recognition

Multimodal biometric recognition has attracted intensive attention due to its significant performance improvement for personal authentication by exploiting multiple resources of data. However, most existing multimodal biometric recognition methods tend to fuse completely different biometric traits, making them hard to explore the complementary features of the multimodal data. In this paper, we propose a new multimodal biometric descriptor by jointly learning the multi-type collaborative features of multi-modality finger-knuckle-print (FKP) images. First, we form multi-type feature vectors to capture the texture and direction patterns of the multimodal FKP images. Then, we jointly learn the feature projection to map multi-type vectors into a compact FKP descriptor. Moreover, our method automatically selects the optimal weights for multi-type features during feature learning to make the learned feature codes discriminative. Lastly, we integrate the non-overlapping block-wise histograms of the learned binary codes as the final multimodal FKP feature descriptor. The experimental results conducted on the benchmark PolyU FKP database demonstrate the effectiveness of the proposed method for multimodal FKP recognition.

A Federated Attention-Based Multimodal Biometric Recognition Approach in IoT.

The rise of artificial intelligence applications has led to a surge in Internet of Things (IoT) research. Biometric recognition methods are extensively used in IoT access control due to their convenience. To address the limitations of unimodal biometric recognition systems, we propose an attention-based multimodal biometric recognition (AMBR) network that incorporates attention mechanisms to extract biometric features and fuse the modalities effectively. Additionally, to overcome issues of data privacy and regulation associated with collecting training data in IoT systems, we utilize Federated Learning (FL) to train our model This collaborative machine-learning approach enables data parties to train models while preserving data privacy. Our proposed approach achieves 0.68%, 0.47%, and 0.80% Equal Error Rate (EER) on the three VoxCeleb1 official trial lists, performs favorably against the current methods, and the experimental results in FL settings illustrate the potential of AMBR with an FL approach in the multimodal biometric recognition scenario.

Advanced Audio Signal Processing for Speaker Recognition and Sentiment Analysis

Abstract: Automatic Speech Recognition (ASR) technology has revolutionized human-computer interaction by allowing users to communicate with computer interfaces using their voice in a natural way. Speaker recognition is a biometric recognition method that identifies individuals based on their unique speech signal, with potential applications in security, communication, and personalization. Sentiment analysis is a statistical method that analyzes unique acoustic properties of the speaker's voice to identify emotions or sentiments in speech. This allows for automated speech recognition systems to accurately categorize speech as Positive, Neutral, or Negative. While sentiment analysis has been developed for various languages, further research is required for regional languages. This project aims to improve the accuracy of automatic speech recognition systems by implementing advanced audio signal processing and sentiment analysis detection. The proposed system will identify the speaker's voice and analyze the audio signal to detect the context of speech, including the identification of foul language and aggressive speech. The system will be developed for the Marathi Language dataset, with potential for further development in other languages.

A novel approach for biometric recognition based on ECG feature vectors

In the field of biometrics, electrocardiogram (ECG) recognition has the advantages of being live, difficult to replicate, and easy to collect. However, most deep learning methods for biometric recognition use a fixed length of ECG beats and only identify ECGs from a fixed number of people. The fixed input length may lead to information loss and the model needs to be retrained when a new ECG is added to the database, which is inconvenient for real applications. In this paper, we propose a biometric recognition method based on an ECG feature vector that uses a pooling layer to accept variable-length beat signals. The method accepts new classes without requiring model retraining while achieving excellent recognition performance. We verified the robustness of the method in the ECG-ID database, the MITDB arrhythmia database, and a private USSTDB database consisting of the ECG records of 60 volunteers before and after exercise. In addition, this method has good recognition ability in cross-database tests. The proposed method reduces the gap in biometric identification of exercise ECGs and demonstrates robustness and good generalization.

Biometric face recognition method using graphics processing unit system

The expansion of biometric applications and databases is worrying. Processing extensive or sophisticated biometric data results in longer wait times, which might restrict application usefulness. This work focuses on accelerating the processing of biometric data and proposes a parallel method of data processing that exceeds the capabilities of a central processing unit (CPU). The combination of the graphics processing unit (GPU) and compute unified device architecture (CUDA) results in at least three times the processing speed of a published accurate and secure multimodal biometric system. The GPU-assisted approach beats the CPU-only implementation when saturating the CPU-only performance with more people than the available thread count. The GPU-assisted solution is also proven to have the same accuracy as the original system, indicating accuracy and processing performance improvements in the demanding big data environment.

Gait phase recognition of lower limb exoskeleton system based on the integrated network model

Exoskeleton robots have become an emerging technology in medical, industrial and military applications. Human gait phase recognition is the crucial technology for recognizing movement intention of the exoskeleton wearer and controlling the exoskeleton robot. As a new biometric recognition method, gait phase recognition also plays an important role in clinical disease diagnosis, rehabilitation training and other fields. This paper proposes an integrated network model SBLSTM that combines sparse autoencoder (SAE), bidirectional long short-term memory (BiLSTM) and deep neural network (DNN) aiming at gait phase recognition during human movement. The model can accurately identify four phases in the gait cycle, including heel strike (HS), foot flat (FF), heel off (HO) and swing phase (SW). Normalization and feature extraction of collected sensor signals are performed to enhance the accuracy of recognition during the gait identification process. The processed data are input into the SBLSTM model. The introduction of SAE into the model can extract key information from gait characteristics. BiLSTM is used to learn temporal patterns and periodic changes in gait data. DNN is adopted to identify gait phases and output classification results. Different algorithms such as DNN, LSTM and SBLSTM are applied to the gait phase detection of subjects. The experimental results show that the SBLSTM algorithm is effective in gait recognition. The accuracy and F-score are outperformed by other algorithms, which verifies the effectiveness of the SBLSTM in practice.

A wavelet-based capsule neural network for ECG biometric identification

Electrocardiogram (ECG) signals have received a high level of attention from the biometric research community due to their unique nature for each person, which makes them suitable for developing accurate and reliable human identification systems. Although most existing ECG-based biometric recognition methods have received prominent results, several consecutive heartbeat segments are used in their approaches to achieve high accuracy, which is challenging to apply in biometric systems deployed in real-world applications. This paper proposes a new approach for human identification via ECG, based on a combination of Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) along with a novel kind of deep learning technique known as Capsule network. The CWT is used to transform a single heartbeat signal into the time–frequency domain, and the DWT is adopted to extract spectral information of 2D frequency-time scalogram images to further improve accuracy. The discrete wavelet coefficients are then used as input to the capsule network promoting the recognition performance due to its high learning capacities. To support real-life practicality of ECG biometric identification system, the effectiveness and efficiency of our approach were evaluated over four databases that include normal and abnormal ECG records: PTB Diagnosis ECG (PTB), MIT-BIH Arrhythmia, MIT-BIH Normal Sinus Rhythm (NSRDB), and the MIT-BIH ST Change (STDB) databases. Experimental results demonstrate that our proposed method was able to achieve high identification accuracies and outperforming other state-of-the-art methods, by achieving an accuracy of 99.5%, 98.1%, 98.2%, and 100% on the PTB, MIT-BIH arrhythmia, STDB, and NSRDB respectively. Furthermore, the approach showed very good generalization ability since the training and test sets were completely different, which demonstrates the feasibility to promote the application of our approach in practice.

Time–frequency fusion learning for photoplethysmography biometric recognition

Photoplethysmography (PPG) signal is a novel biometric trait related to the identity of people; many time- and frequency-domain methods for PPG biometric recognition have been proposed. However, the existing domain methods for PPG biometric recognition only consider a single domain or the feature-level fusion of time and frequency domains, without considering the exploration of the fusion correlations of the time and frequency domains. The authors propose a time–frequency fusion for a PPG biometric recognition method with collective matrix factorisation (TFCMF) that leverages collective matrix factorisation to learn a shared latent semantic space by exploring the fusion correlations of the time and frequency domains. In addition, the authors utilise the ℓ2,1 norm to constrain the reconstruction error and shared matrix, which can alleviate the influence of noise and intra-class variation, and ensure the robustness of learnt semantic space. Experiments demonstrate that TFCMF has better recognition performance than current state-of-the-art methods for PPG biometric recognition.

Multifeature Deep Cascaded Learning for PPG Biometric Recognition

Aiming at the problem that the traditional photoplethysmography (PPG) biometric recognition based on sparse representation is not robust to noise and intraclass variations when the sample size is small, we propose a PPG biometric recognition method based on multifeature deep cascaded sparse representation (MFDCSR). The method consists of multifeature signal coding and deep cascaded coding. The function of multifeature signal coding is to extract the shape, wavelet, and principal component analysis features of the PPG signal and to perform sparse representation. Deep cascaded coding is multilayer feature coding. Each layer combines multifeature signal coding with the result of the previous layer as input, and the output of each layer is the input of the next layer. The function of deep cascade coding is to learn the features of the PPG signal, layer by layer, and to output the category distribution vector of the PPG signal in the last layer. Experiments demonstrate that MFDCSR has better recognition performance than current methods for PPG biometric recognition.

Vein Biometric Recognition Methods and Systems: A Review

Vein Biometric Recognition Methods and Systems: A Review

Multispectral hand features for secure biometric authentication systems

SummaryWith rapid growth of mobile‐based computing, reliable security and user authentication methods are necessary. This article proposes a proof‐of‐concept biometric authentication method utilizing hand images collected in different light spectra. An analysis of similarity between pattern of blood vessels extracted from near‐infrared images and thermal images and assessment of the correlation between individual biometric features contained in each image type is also performed. Results indicate a large potential of using thermal images in biometrics more extensively than now. Also proposed and evaluated are biometric recognition methods based on images of the hand acquired in visible light, near infrared, and using thermal infrared sensors. Two approaches were used to assess information content in images of each type: one based on texture descriptor and one employing convolutional neural networks. In an evaluation gathering data from 104 subjects, the former yielded the lowest equal error rate (EER) of , whereas the latter approach gave for thermal images. Finally, fusion of different‐spectra modalities increases accuracy and further reduces EER to . This is, to the authors' best knowledge, the first study exploring the concept of fusing different spectral representations of the human hand for the purpose of biometric recognition.

Towards Protecting and Enhancing Vascular Biometric Recognition Methods via Biohashing and Deep Neural Networks

Biometric template protection has been a crucial concern in biometric recognition systems. This is because biometric characteristics are irreplaceable, so the compromised templates can also be used in other applications. On the other side, deploying template protection algorithms often affects the performance of biometric systems. In this paper, we consider both raw and pre-processed finger vein images and propose a novel deep-learning-based framework to protect biometric templates and enhance recognition performance. We use a deep convolutional auto-encoder structure to reduce the dimension of the feature space, and then secure templates by applying the Biohashing algorithm on the features extracted at the bottleneck layer of our auto-encoder. The experimental results indicate that the protected templates through our framework achieve superior performance than Biohash protected templates of the raw features in the normal scenario. In the stolen scenario, where the Biohashing key is stolen, our model yields far better performance than Biohashing of raw features extracted by previous recognition methods. We also evaluate the generalization of our proposed framework on other vascular biometric modalities. It is worth mentioning that we provide an open-source implementation of our framework so that other researchers can verify our findings and build upon our work.

An exploratory study of interpretability for face presentation attack detection

Biometric recognition and presentation attack detection (PAD) methods strongly rely on deep learning algorithms. Though often more accurate, these models operate as complex black boxes. Interpretability tools are now being used to delve deeper into the operation of these methods, which is why this work advocates their integration in the PAD scenario. Building upon previous work, a face PAD model based on convolutional neural networks was implemented and evaluated both through traditional PAD metrics and with interpretability tools. An evaluation on the stability of the explanations obtained from testing models with attacks known and unknown in the learning step is made. To overcome the limitations of direct comparison, a suitable representation of the explanations is constructed to quantify how much two explanations differ from each other. From the point of view of interpretability, the results obtained in intra and inter class comparisons led to the conclusion that the presence of more attacks during training has a positive effect in the generalisation and robustness of the models. This is an exploratory study that confirms the urge to establish new approaches in biometrics that incorporate interpretability tools. Moreover, there is a need for methodologies to assess and compare the quality of explanations.