Biometric Features Research Articles

Personal Identification Using Palm Features Recognition

Personal recognition is meant for finding a way for establishment of connections between the person and his/her biometrical features. Such system is depending various data types such as facial images, voice and limbs. In this paper, palm print recognition is made using deep learning paradigms such as feed forward neural network (FFNN). The palm features are extracted by tracking the principal lines of palm skin. This involves performing of pixel to pixel analysis by comparing pixel value with its four sides neighbors. FFNN model is tuned up using ABC-KNN algorithm then used for classification. The proposed system has yielded good recognition accuracy score of 98.66%.

HDL-PI: hybrid DeepLearning technique for person identification using multimodal finger print, iris and face biometric features

HDL-PI: hybrid DeepLearning technique for person identification using multimodal finger print, iris and face biometric features

Different Biometrical Features for Detecting Human Intrusion Using Artificial Intelligence: Literature Review

Many cases of theft and property trespass in addition to crimes occur in the world after these people break into people's homes and buildings illegally, so this article aims to shed light on most of the smart methods and computer technologies used in identifying people that help to reduce these crimes. Where the diversity of biometric traits was relied upon, such as fingerprint, handprint, ear, face, texture, some deformations characteristic of people, eye, footprints, DNA analysis and other important biometric traits. Also, many intelligent algorithms were used to identify these traits.

A novel cancellable biometric recognition system based on Rubik’s cube technique for cyber-security applications

ObjectiveTen years ago, biometrics became an efficient tool of security and authentication enforcement. In a classical access system, a user can easily change password if it is compromised. However, user’s biometrics are limited and unique, and if a user biometric trait is compromised, it will be impossible to change it. Hence, cancellable biometric systems depend on the transformation of data or a biometric feature into a new format, so that users can replace their biometric templates in the same or a different system. In this paper, an efficient cancellable biometric recognition system is presented. In this system, both signal-and feature-level transformations are used to generate the cancellable templates MethodsA novel hybrid encryption framework based on the Rubik’s cube technique is presented to allow efficient encryption of face, iris, and fingerprint biometric templates. This framework begins with extraction of biometric features using the Scale-Invariant Feature Transform (SIFT). After that, the resulting biometric features are encrypted with the optical Double Random Phase Encoding (DRPE) algorithm. Then, the resulting encrypted biometric features are further encrypted with the chaotic Baker map permutation, Advanced Encryption Standard (AES), or Ron's Code (RC6) algorithm in the Cipher Feed Back (CFB) mode of operation. Finally, the resulting encrypted biometrics are additionally encrypted with the Rubik’s cube technique. The chaotic, RC6, or AES encrypted biometrics are utilized on the faces of the Rubik’s cube. From the concepts of image encryption, the DRPE, RC6, and the AES algorithms introduce a degree of diffusion, whilst the chaotic Baker map adds a degree of permutation. Moreover, the Rubik’s cube technique adds more permutation to the encrypted biometrics, simultaneously. The quality evaluation of the proposed cancellable biometric system reveals good performance. ResultsThe simulation results prove that the suggested hybrid encryption framework is reliable, and it presents recommended security and robustness levels for its utilization for building efficient cancellable biometric systems compared to the traditional techniques.

AFHIRIS: African Human Iris Dataset (Version 1)

Biometric systems remain the most widely used methods for identification and authentication purposes. Their wide acceptability has opened up more research into new application areas of biometric systems. However, biometric research requires an appropriate biometric dataset to validate the proposed technique. This dataset could be privately owned or publicly available for research purposes. In the field of iris biometric research, the iris dataset produced by the Chinese Academy of Sciences (CASIA) is the first, most popular, and widely used publicly available iris dataset. However, the increasing popularity and acceptability of human iris-related research have called for additional benchmarks, and therefore, new publicly available databases of human iris images. Existing publicly available human iris datasets have been collected from non-African subjects; therefore, this dataset is the first publicly available human iris dataset of African descent. Three categories of images were collected from 1028 volunteers that participated in the data collection task. The first category was made up of four iris images that were captured when the volunteers used spectacles, while the second category includes four iris images that were captured when the volunteers wore no spectacles. However, the third category of iris images was obtained from eight volunteers that used print-patterned contact lenses. Only four images were captured from volunteers in this category as they were not asked to put on spectacles. In addition to the iris images captured, soft biometric features such as age, gender, state of origin, weight, and height of the volunteers were also captured. It is strongly believed that this unique collection of iris datasets of African descent will open up new research in the study of the human iris.

Cattle body detection based on YOLOv5-ASFF for precision livestock farming

Precision livestock farming is a hot topic in the field of agriculture at present. However, due to the diversity of breeding environments, the current intelligent monitoring of animal information still faces challenges. In this study, a YOLOv5-ASFF object detection model was proposed to detect cattle body parts (e.g. individual, head, legs) in complex scenes. The proposed YOLOv5-ASFF consists of two components: YOLOv5 responsible for extracting multi-scale features from sample images, while ASFF was used to adaptively learn fused spatial weights for each scale feature map and fully acquire the features. In this way, the cattle area detection was realized and the generalization of detection model was improved. To verify the applicability and robustness of YOLOv5-ASFF, a challenging dataset consisting of cattle (cow and beef) with complex environments (e.g. different lighting, occlusion, different depths of field, multiple targets and small targets) was constructed for experimental testing. The proposed method based on YOLOv5-ASFFachieved a precision of 96.2%, a recall of 92%, an F1 score of 94.1%, and an mAP@0.5 of 94.7% on this dataset, which outperformed Faster R-CNN, Cascade R-CNN, SSD, YOLOv3 and YOLOv5s. Experimental results showed that the YOLOv5-ASFF method could fully learn more animal biometric visual features, thereby improving the performance of cattle detection model, especially the detection of key parts. Overall, the proposed deep learning-based cattle detection method is favorable for long-term autonomous cattle monitoring and management in intelligent livestock farming.

OdorTAM: Technology Acceptance Model for Biometric Authentication System Using Human Body Odor

Body odor is a biometric feature unique to each individual, and it can be used for authentication. However, decision makers must learn about the users' level of acceptance of this technology, as well as their thoughts on the system's features and procedures. In this study, a technology acceptance model (TAM) for body-odor-based biometric techniques named OdorTAM was proposed and validated. An English language questionnaire was developed in a web-based, easy-to-read format on Google Forms. The survey consisted of 19 questions, and 150 responses were received. Statistical analysis of the responses was carried out, and it was found that all the hypotheses were supported. Therefore, the OdorTAM model appears to be satisfactory. To this end, we posit that a body-odor-based biometric technique can be one of the alternatives for authentication, and it can also be used along with some other techniques for improved security. The study contributes to the literature on consumers' understanding of biometric technologies, in particular odor detection, which has received relatively less attention in extant research.

Differentiation and Propagation Potential of Arnica montana L. Achenes as a Consequence of the Morphological Diversity of Flowers and the Position of Flower Heads on the Plant.

Arnica montana L. is a very important medicinal plant and simultaneously a European endemic endangered plant species. The morphological features and details of seed development and achene variability are poorly recognized. The aim of this study was to determine the impact of the achene position in the infructescence and the location of the inflorescence on the plant on the (i) morphological characteristics and germination ability of achenes, and (ii) recruitment of seedlings and their biometric features. Infructescences containing fully ripe achenes were randomly collected from A. montana individuals for the measurements and the germination experiment. Scanning electron microscopy, fluorescence microscopy, and light microscopy were used for characterization of flowers and achenes. The morphological traits of achenes and reproductive characteristics of A. montana were determined by the position of the achenes in the infructescence and the location of the inflorescence on the plant. The surface of arnica achenes is equipped with non-glandular and glandular trichomes, which is very rarely presented in species of the family Asteraceae. It is possible that the fluid-containing glandular trichomes are a source of essential oils. The peripherally located achenes were longer, thinner, and lighter. They were characterized by lower embryo weight, lower embryo/achene weight ratio, and lower germination capacity in comparison to the centrally located ones. The results presented in this article fill the gap in the knowledge of the morphology of achenes and the biology of the species, and provide information that can help in breeding programs, active protection, and field cultivation.

EXTRACTION AND RECOVERING OF FINGER VEIN VERIFICATION BASED ON DEEP ATTRIBUTE REPRESENTATION

A finger vein authentication system is proposed in this research. Biometrics is the science of determining a person's identity based on physiological or behavioral characteristics. Physical characteristics like fingerprints, a face or a retina, as well as personal characteristics like a signature, are included in these characteristics. Biometric features are significantly more difficult for attackers to replicate or fabricate than traditional methods, and they are extremely rare to lose. Biometric traits are used in the identification system, which increases security and dependability. The technology to verify vein patterns is still relatively new, compared with other human characteristics. The proposed work focuses on developing a contactless sensor to retrieve features from the hand's finger vein pattern using a Deep attribute Representation based Fractional Firefly method (DAR-FFF). Vein pattern identification scans the blood for hemoglobin using an infrared light source. After the participant's palm is placed over the sensing device, an infrared region beam from the device measures the orientation of the arteries. These ultraviolet wavelengths are absorbed by liquid hemoglobin in the vasculature, resulting in dark streaks on the map. The hand's finger has more intricate circulatory pathways and a variety of distinguishing characteristics. Image enhancement, skeletonization, and vein pattern chain code comparison are all processes in this procedure.

On the Impact of Multimodal and Multisensor Biometrics in Smart Factories

Smart factories are fostered by integrating intelligent systems and ICT technologies. The role they play is crucial in the spread of Industry 4.0 and the economic growth of developed countries. Smart factories can be empowered by using several sensors aimed at making them more and more “smart.” Unfortunately, work accidents are still very common resulting in human losses and permanent injuries. This makes it urgent and key to implementing security and safety measures also in the context of a smart factory. In this article, a novel framework for supporting smart devices in a smart factory, using multiple sensors to monitor different biometric features, both physical and behavioral is proposed. Thanks to the fusion of several biometric traits with the support of machine learning technologies working together with different kinds of sensors, it is possible to guarantee three fundamental aspects within the interaction between an operator of a smart device and the device itself: continuous authentication (i.e., continuous face recognition), drowsiness detection, and liveness detection. With the application of the proposed framework, it is possible to significantly improve the safety of operators avoiding fatal accidents for them. Experiments made using COTS-hardware showed that the authors’ idea is easy to implement in a large-scale smart factory and further improves the spread of Industry 4.0.

Modelling level I Axillary Lymph Nodes depth for Microwave Imaging

Purpose:Patient-specific information on the depth of Axillary Lymph Nodes (ALNs) is important for the development of new diagnostic imaging technologies, e.g. Microwave Imaging (MWI), aiming to assess the diagnosis of ALNs during breast cancer staging. Studies about ALNs depth have been presented for treatment planning, but they lack information on sample size and usability of the data to infer the depth of ALNs. The aim of this study was to create a mathematical model that can be used to predict a depth interval where level I ALNs are likely to be located. Methods:We extracted biometric features of 98 patients who underwent breast Magnetic Resonance Imaging (MRI) to train two types of regression models. We then tested different combination of features to predict ALNs depth and found the best predictor. The final prediction models were then implemented in an algorithm used for MWI and tested with anthropomorphic phantoms of the axillary region. Results:Body Mass Index (BMI) was the feature with best performance to predict ALNs depth with coefficient of determination (R2) ranging from 0.49 to 0.55 and Root Mean Squared Error (RMSE) ranging from 0.68 to 0.91 cm. The proposed model showed satisfactory results in microwave images of patients with different BMIs. Conclusions:The presented results contribute to the development of reconstruction algorithms for new imaging technologies and to the assessment of ALNs in other medical applications.

MmSafe: A Voice Security Verification System Based on Millimeter-Wave Radar

With the increasing popularity of smart devices, users can control their mobile phones, TVs, cars, and smart furniture by using voice assistants, but voice assistants are susceptible to intrusion by outsider speakers or playback attacks. In order to address this security issue, a millimeter-wave radar-based voice security authentication system is proposed in this paper. First, the speaker's fine-grained vocal cord vibration signal is extracted by eliminating static object clutter and motion effects; second, the weighted Mel Frequency Cepstrum Coefficients (MFCCs) are obtained as biometric features; and finally, text-independent security authentication is performed by the WMHS (Weighted MFCCs and Hog-based SVM) method. This system is highly adaptable and can authenticate designated speakers, resist intrusion by other unspecified speakers as well as playback attacks, and is secure for smart devices. Extensive experiments have verified that the system achieves a 93.4% speaker verification accuracy and a 5.8% miss detection rate for playback attacks.

Biometric radiographic features between foot lesions and long toe under-run heel in donkeys

AbstractThe present study aimed to document the relationship between biometric foot measurements and different radiographic lesions in a group of donkeys' feet with long toe under-run heel. The common radiographic foot lesions were pedal osteitis complex in 26 (86.6%) and navicular bone reaction in 8 (26.7%). Osteophyte reaction was identified at proximal phalanx in 8 (26.7%) and middle phalanx in 10 (33.3%). Pastern and coffin joints osteoarthritis were found in 4 (13.3%) and 9 (30%) respectively. Palmar soft tissue calcification and eminence of 2nd phalanx were reported in 5 (16.6%) and 1 (3.3%) respectively. The dorsal hoof wall angle (DHWA) was positive significantly correlated with the axis of the distal phalanx, distal phalanx and proximal palmar cortex angles, the heel angle, and distal phalanx solar border angle. The increased dorsal hoof wall length (DHWL) was positive strongly correlated with dorsal coronary band height, medial wall length, lateral wall length, lateral coronary band distance, medial coronary band distance, lateral distal phalanx to bottom distance, and medial distal phalanx to bottom distance. The dorsal hoof wall and heel angles reflected increased stress on the hoof capsule, distal interphalangeal joint, navicular bone, and palmar soft tissue structures. The low heel, the distal phalanx solar border angle and under development of the palmar section of the hoof indicated distal phalanx was slightly slippage dorsally, increased tension on the DDFT, navicular bone, and pedal osteitis complex in long toe under-run heels. This study demonstrated a relationship between biometric foot measurements and the presence of radiographic foot lesions detected in long toe under-run heel in donkeys.

Hybrid Biometric Based Person Identification Using Machine Learning

When compared to the more traditional methods of authentication, biometric systems offer a much higher level of protection for a wide range of uses (like pin, passwords etc.). Various sectors of modern society can find use for biometric systems. Among these are authentication for computers, attendance tracking for businesses, financial transactions, safeguarding private information, securing access to buildings, and ensuring the safety of travellers at airports. Identifying and verifying individuals via their unique physical and behavioural characteristics is the primary function of the biometric system. Importance of biometric systems in modern society is analysed in this paper. Single-trait biometric user recognition is currently used, but it does not offer sufficient security for critical programmes. Multimodal biometric systems are used to get around these issues. Physical and behavioural characteristics, like fingerprints and DNA, signatures and fingerprints, etc., are all part of a multimodal biometric system's arsenal for authenticating users. In addition to the hard biometric features already mentioned (skin colour, age, height, hair colour, eye colour, gender, etc.), the use of a person's soft biometric traits is becoming increasingly common. While soft biometrics can be used to boost the efficiency of biometric systems that focus on other characteristics, they are not without their own set of drawbacks, such as a lack of permanence and distinctive behaviour. User authentication, security, and performance are all boosted by the work presented in this paper using Machine learning (F-SVM -Fuzzy-Support Vector Machine) model.

Panic Detection Using Machine Learning and Real-Time Biometric and Spatiotemporal Data

It is common sense that immediate response and action are among the most important terms when it comes to public safety, and emergency response systems (ERS) are technology components strictly tied to this purpose. While the use of ERSs is increasingly adopted across many aspects of everyday life, the combination of them with real-time biometric and location data appears to provide a different perspective. Panic is one of the most important emergency indicators. Until now, panic events of any cause tend to be treated in a local manner. Various attempts to detect such events have been proposed based on traditional methods such as visual surveillance technologies and community engagement systems. The aim of this paper is twofold. First, it presents an innovative multimodal dataset containing biometric and spatiotemporal data associated with the detection of panic state in subjects that perform various activities during a certain period. For this purpose, time-enabled location data are combined with biometrics coming from wearables and smartphones that are analyzed in real-time and produce data indicating possible panic events that are geospatially described. Second, the proposed dataset is used to train various machine learning models, and their applicability to correctly distinguish panic states from normal behavior is thoroughly examined. As a result, the Gaussian SVM classifier ranked first among seven classifiers, achieving an accuracy score of 94.5%. The dataset was also tested in a deep learning framework, achieving an accuracy level of 93.4%. A long short-term memory approach was also used, which reached a top accuracy of 94%. Moreover, the contribution of the various biometric and geospatial features is analyzed in-depth to determine their partial importance in the overall panic detection process. This is moving towards the creation of a smart geo-referenced ERS that could be used to inform the authorities regarding a potentially unpleasant event by detecting possible crowd panic patterns and helping to act accordingly, getting the information right from the source of the event, the human body. The proposed dataset is freely distributed to the scientific community under the third version of GNU General Public License (GPL v3) through the GitHub platform.

Influence of soil physicochemical properties on biometrical and physical features of persian oak wood

Influence of soil physicochemical properties on biometrical and physical features of persian oak wood

On the effect of selfie beautification filters on face detection and recognition

Beautification and augmented reality filters are very popular in applications that use selfie images captured with smartphones or personal devices. However, they can distort or modify biometric features, severely affecting the capability of recognizing individuals' identity or even detecting the face. Accordingly, we address the effect of such filters on the accuracy of automated face detection and recognition. The social media image filters studied either modify the image contrast or illumination or occlude parts of the face with for example artificial glasses or animal noses. We observe that the effect of some of these filters is harmful both to face detection and identity recognition, specially if they obfuscate the eye or (to a lesser extent) the nose. To counteract such effect, we develop a method to reconstruct the applied manipulation with a modified version of the U-NET segmentation network. This is observed to contribute to a better face detection and recognition accuracy. From a recognition perspective, we employ distance measures and trained machine learning algorithms applied to features extracted using a ResNet-34 network trained to recognize faces. We also evaluate if incorporating filtered images to the training set of machine learning approaches are beneficial for identity recognition. Our results show good recognition when filters do not occlude important landmarks, specially the eyes (identification accuracy >99%, EER<2%). The combined effect of the proposed approaches also allow to mitigate the effect produced by filters that occlude parts of the face, achieving an identification accuracy of >92% with the majority of perturbations evaluated, and an EER <8%. Although there is room for improvement, when neither U-NET reconstruction nor training with filtered images is applied, the accuracy with filters that severely occlude the eye is 12% (EER)

Features extraction of fingerprints based on hybrid particle swarm optimization and bat algorithms

Most security system's essential errand is to check that the people are in fact who they claim to be. In Contrast to traditional techniques such as passwords and smart cards that are used in some organizations, fingerprint identification may be preferred as it makes the information virtually impossible to steal. The most extensive used biometric features are Fingerprints, in order to identify a person because of their uniqueness and invariance. The fingerprint consists of valleys and ridges on the surface of a fingertip. In this paper, a new hybrid strategy Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed to extract features from fingerprint images. Both PSO and BA algorithms are swarm-based algorithms that mimics the swarm behaviour of particles and bats in nature. In the field of image processing, features are extremely significant. Before obtaining features, the noisy area should be removed from the foreground first, and then several important techniques are applied on each sample image in the database such as Fingerprint Enhancement by using Fast Fourier Transform (FFT), Binarization, and Thinning. The hybrid (PSO-BA) algorithm is proposed as a pre-enhancing step to select the clear minutiae (or feature) structures across several iterations, which will be more suited for the matching phase. By comparing the proposed method with several methods in calculating FAR and FRR, the results showed that the FAR (0.001) and FRR (0.01) were less than the other proposed methods. That means the hybrid (PSO-BA) algorithm has the better results, which means it can be used as one of the best search approaches to extract features from fingerprints

Multimodal ensemble model for Alzheimer's disease conversion prediction from Early Mild Cognitive Impairment subjects

Alzheimer's Disease (AD) is the most common type of dementia. Predicting the conversion to Alzheimer's from the mild cognitive impairment (MCI) stage is a complex problem that has been studied extensively. This study centers on individualized EMCI (the earliest MCI subset) to AD conversion prediction on multimodal data such as diffusion tensor imaging (DTI) scans and electronic health records (EHR) for their patients using the combination of both a balanced random forest model alongside a convolutional neural network (CNN) model. Our random forest model leverages EHR's patient biometric and neuropsychiatric test score features, while our CNN model uses the patient's diffusion tensor imaging (DTI) scans for conversion prediction. To accomplish this, 383 Early Mild Cognitive Impairment (EMCI) patients were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Within this set, 49 patients would eventually convert to AD (EMCI_C), whereas the remaining 335 did not convert (EMCI_NC). For the EHR-based classifier, 288 patients were used to train the random forest model, with 95 set aside for testing. For the CNN classifier, 405 DTI images were collected across 90 distinct patients. Nine clinical features were selected to be combined with the visual predictor. Due to the imbalanced classes, oversampling was performed for the clinical features and augmentation for the DTI images. A grid search algorithm is also used to determine the ideal weighting between our two models. Our results indicate that an ensemble model was effective (98.81% accuracy) at EMCI to AD conversion prediction. Additionally, our ensemble model provides explainability as feature importance can be assessed at both the model and individual prediction levels. Therefore, this ensemble model could serve as a diagnostic support tool or a means for identifying clinical trial candidates.

An Efficient and Lightweight Method for Human Ear Recognition Based on MobileNet

In recent years, biometric recognition patterns have attracted the attention of many researchers, among which human ears, as a unique and stable biometric feature, have significant advantages in verifying personal identity. In the Internet era, a system with low computing cost and good real-time performance is more popular. Most of the existing ear recognition methods are based on a large parameter network model, which causes a large memory footprint and computational overhead. This paper proposes an efficient and lightweight human ear recognition method (ELERNet) based on MobileNet V2. Based on the MobileNet V2 model, dynamic convolution decomposition is introduced to enhance the representation ability of human ear features. Then, combined with the coordinate attention mechanism, the spatial features of human ear images are aggregated to locate the location information of the human ear features more accurately. We conducted experiments on AWE and EarVN1.0 human ear datasets. Compared with the MobileNet V2 model, the recognition accuracy of our method is significantly improved. Using less computing hardware resources, the ELERNet model achieves 83.52% and 96.10% Rank-1 (R1) recognition accuracy, respectively, which is better than other models. Finally, we provide a visual interpretation using GradCAM technology, and the results show that our method can learn specific and discriminative features in the ear images.