Finger Vein Template Protection with Directional Bloom Filter

Biometrics has become a widely accepted solution for secure user authentication. However, the use of biometric traits raises serious concerns about the protection of personal data and privacy. Traditional biometric systems are vulnerable to attacks due to the storage of original biometric data in the system. Because biometric data cannot be changed once it has been compromised, the use of a biometric system is limited by the security of its template. To protect biometric templates, this paper proposes the use of directional bloom filters as a cancellable biometric approach to transform the biometric data into a non-invertible template for user authentication purposes. Recently, Bloom filter has been used for template protection due to its efficiency with small template size, alignment invariance, and irreversibility. Directional Bloom Filter improves on the original bloom filter. It generates hash vectors with directional subblocks rather than only a single-column subblock in the original bloom filter. Besides, we make use of multiple fingers to generate a biometric template, which is termed multi-instance biometrics. It helps to improve the performance of the method by providing more information through the use of multiple fingers. The proposed method is tested on three public datasets and achieves an equal error rate (EER) as low as 5.28% in the stolen or constant key scenario. Analysis shows that the proposed method meets the four properties of biometric template protection. Doi: 10.28991/HIJ-2023-04-02-013 Full Text: PD

Fall Detection and Motion Analysis Using Visual Approaches

Falls are considered one of the most ubiquitous problems leading to morbidity and disability in the elderly. This paper presents a vision-based approach toward the care and rehabilitation of the elderly by examining the important body symmetry features in falls and activities of daily living (ADL). The proposed method carries out human skeleton estimation and detection on image datasets for feature extraction to predict falls and to analyze gait motion. The extracted skeletal information is further evaluated and analyzed for the fall risk factors in order to predict a fall event. Four critical risk factors are found to be highly correlated to falls, including 2D motion (gait speed), gait pose, 3D trunk angle or body orientation, and body shape (width-to-height ratio). Different variants of deep architectures, including 1D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) Network, Gated Recurrent Units (GRU) model, and attention-based mechanism, are investigated with several fusion techniques to predict the fall based on human body balance study. A given test gait sequence will be classified into one of the three phases: non-fall, pre-impact fall, and fall. With the attention-based GRU architecture, an accuracy of 96.2% can be achieved for predicting a falling event

A review of abnormal behavior detection in activities of daily living

Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend

Non-invasive health prediction from visually observable features [version 2; peer review: 1 approved, 1 approved with reservations]

Background: The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person’s health. Methods: In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set. Results: Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time. Conclusions: The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Palmprint Recognition For Personal Identification System

Palmprint is well-known for several advantages such as stable line features, low-resolution imaging, low-cost capturing device, and user-friendly. In this thesis, an automated scanner-based palmprint recognition system is proposed. The system automatically captures and aligns the palmprint images for processing. The proposed palmprint recognition system consists of two important stages namely the enrollemnt and recognition components. In the enrollment stage, the palmprint images acquired are first aligned by the pre-processing step. After that, important palmprint features are extracted to generate matching templates to be stored in the database. In the verification stage, a new palam print image is also processed bt the pre-processing and feature extraction module, and then matched with the stored reference template to determine whether it belongs to a genuine template

Pose-Based Gait Analysis for Diagnosis of Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disorder that is more common in elderly people and affects motor control, flexibility, and how easily patients adapt to their walking environments. PD is progressive in nature, and if undetected and untreated, the symptoms grow worse over time. Fortunately, PD can be detected early using gait features since the loss of motor control results in gait impairment. In general, techniques for capturing gait can be categorized as computer-vision-based or sensor-based. Sensor-based techniques are mostly used in clinical gait analysis and are regarded as the gold standard for PD detection. The main limitation of using sensor-based gait capture is the associated high cost and the technical expertise required for setup. In addition, the subjects’ consciousness of worn sensors and being actively monitored may further impact their motor function. Recent advances in computer vision have enabled the tracking of body parts in videos in a markerless motion capture scenario via human pose estimation (HPE). Although markerless motion capture has been studied in comparison with gold-standard motion-capture techniques, it is yet to be evaluated in the prediction of neurological conditions such as PD. Hence, in this study, we extract PD-discriminative gait features from raw videos of subjects and demonstrate the potential of markerless motion capture for PD prediction. First, we perform HPE on the subjects using AlphaPose. Then, we extract and analyse eight features, from which five features are systematically selected, achieving up to 93% accuracy, 96% precision, and 92% recall in arbitrary views

Security And Privacy Risk Assessment on Mobile Payment Services

Internet technology enhances smartphone use and mobile payment technology. • Mobile payment apps contain credit and debit card information and allow us to make transactions on our phones. • Mobile payments carry security risks. Cybercriminals exploit technology flaws to steal money

The MMUISD Gait Database and Performance Evaluation Compared to Public Inertial Sensor Gait Databases

Gait recognition is an emerging biometric method that allows an automatic verification of a person by the way he or she walks. This paper presents a new dataset for gait recognition using mobile sensors called MMUISD Gait Database that resembles the real world as closely as possible. The existing public gait databases are acquired in controlled settings. In this study, an Android application is developed to record the human gait signals through inertial measurement unit sensor such as accelerometer and gyroscope with 50 Hz fixed sampling rate. A preliminary evaluation with 80 samples of participant’s data is carried out to assess the gait recognition performance using the new dataset. Time and frequency domain are used to extract gait features from the raw sensors data. The accuracy is assessed using eight classifiers with 10-fold cross validation. The results show that phone positions and orientation affect the gait recognition performance. The MMUISD dataset that introduces such variability provides a good opportunity for researchers to further investigate these challenges