Approach For Human Identification Research Articles

A Deep Learning Method of Human Identification from Radar Signal for Daily Sleep Health Monitoring.

Radar signal has been shown as a promising source for human identification. In daily home sleep-monitoring scenarios, large-scale motion features may not always be practical, and the heart motion or respiration data may not be as ideal as they are in a controlled laboratory setting. Human identification from radar sequences is still a challenging task. Furthermore, there is a need to address the open-set recognition problem for radar sequences, which has not been sufficiently studied. In this paper, we propose a deep learning-based approach for human identification using radar sequences captured during sleep in a daily home-monitoring setup. To enhance robustness, we preprocess the sequences to mitigate environmental interference before employing a deep convolution neural network for human identification. We introduce a Principal Component Space feature representation to detect unknown sequences. Our method is rigorously evaluated using both a public data set and a set of experimentally acquired radar sequences. We report a labeling accuracy of 98.2% and 96.8% on average for the two data sets, respectively, which outperforms the state-of-the-art techniques. Our method excels at accurately distinguishing unknown sequences from labeled ones, with nearly 100% detection of unknown samples and minimal misclassification of labeled samples as unknown.

Biometric Security: A Novel Ear Recognition Approach Using a 3D Morphable Ear Model.

Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a deep learning-based algorithm is used for ear detection in 3D side face images. Second, a statistical ear model known as a 3D morphable ear model (3DMEM), was constructed to use as a feature extractor from the detected ear images. Finally, a novel recognition algorithm named you morph once (YMO) is proposed for human recognition that reduces the computational time by eliminating one-to-one registration between probe and gallery, which only calculates the distance between the parameters stored in the gallery and the probe. The experimental results show the significance of the proposed method for a real-time application.

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.

Utility of Circulating Cell-Free DNA in Assessing Microsatellite Instability and Loss of Heterozygosity in Breast Cancer Using Human Identification Approach.

The diagnostic and prognostic utility of circulating cell-free DNA (cfDNA) in breast cancer (BC) patients was recently reported. Here, we investigated the use of cfDNA to examine microsatellite instability (MSI) and loss of heterozygosity (LOH) for early BC diagnosis. cfDNA and genomic DNA from 41 female BC patients and 40 healthy controls were quantified using NanoDrop spectrophotometry and real-time PCR. The stability of genomic and cfDNA was assessed using a high-resolution AmpFlSTR MiniFiler human identification kit. Significant increases in cfDNA plasma concentrations were observed in BC patients compared to controls. The genotype distribution of the eight autosomal short tandem repeat (STR) loci D7S820, D13S317, D21S11, D2S1338, D18S51, D16S539, FGA, and CSF1PO were in Hardy–Weinberg equilibrium. Significant differences in the allele frequencies of D7S820 allele-8, D21S11 allele-29, allele-30.2, allele-32.2, and CSF1PO allele-11 were seen between BC patients and controls. LOH and MSI were detected in 36.6% of the cfDNA of patients compared to genomic DNA. This study highlights the utility of plasma-derived cfDNA for earlier, less invasive, and cost-effective cancer diagnosis and molecular stratification. It also highlights the potential value of cfDNA in molecular profiling and biomarkers discovery in precision and forensic medicine.

Neighborhood Rough Set Approach With Biometric Application

This paper provides a new approach for human identification based on Neighborhood Rough Set (NRS) algorithm with biometric application of ear recognition. The traditional rough set model can just be used to evaluate categorical features. The neighborhood model is used to evaluate both numerical and categorical features by assigning different thresholds for different classes of features. The feature vectors are obtained from ear image and ear matching process is performed. Actually, matching is a process of ear identification. The extracted features are matched with classes of ear images enrolled in the database. NRS algorithm is developed in this work for feature matching. A set of 20 persons are used for experimental analysis and each person is having six images. The experimental result illustrates the high accuracy of NRS approach when compared to other existing techniques.

Forensic odontology - A technology driven approach in human identification

Forensic odontology - A technology driven approach in human identification

Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation.

Nowadays, our lives have benefited from various vision-based applications, such as video surveillance, human identification and aided driving. Unauthorized access to the vision-related data greatly threatens users’ privacy, and many encryption schemes have been proposed to secure images and videos in those conventional scenarios. Neuromorphic vision sensor (NVS) is a brand new kind of bio-inspired sensor that can generate a stream of impulse-like events rather than synchronized image frames, which reduces the sensor’s latency and broadens the applications in surveillance and identification. However, the privacy issue related to NVS remains a significant challenge. For example, some image reconstruction and human identification approaches may expose privacy-related information from NVS events. This work is the first to investigate the privacy of NVS. We firstly analyze the possible security attacks to NVS, including grayscale image reconstruction and privacy-related classification. We then propose a dedicated encryption framework for NVS, which incorporates a 2D chaotic mapping to scramble the positions of events and flip their polarities. In addition, an updating score has been designed for controlling the frequency of execution, which supports efficient encryption on different platforms. Finally, extensive experiments have demonstrated that the proposed encryption framework can effectively protect NVS events against grayscale image reconstruction and human identification, and meanwhile, achieve high efficiency on various platforms including resource-constrained devices.

The Triad Approach for Human Identification: The Role of Biological Anthropologists in Collaborative Forensic Genetic Genealogy Efforts

Forensic anthropologists and bioarchaeologists (FAs/BAs) are often the curators (and researchers) of the most difficult types of unidentified human remains: skeletonized, fragmented, mummified, bur...

ECG-Based Authentication Using Timing-Aware Domain-Specific Architecture

Electrocardiogram (ECG) biometric authentication (EBA) is a promising approach for human identification, particularly in consumer devices, due to the individualized, ubiquitous, and easily identifiable nature of ECG signals. Thus, computing architectures for EBA must be accurate, fast, energy efficient, and secure. In this article, first, we implement an EBA algorithm to achieve 100% accuracy in user authentication. Thereafter, we extensively analyze the algorithm to show the distinct variance in execution requirements and reveal the latency bottleneck across the algorithm's different steps. Based on our analysis, we propose a domain-specific architecture (DSA) to satisfy the execution requirements of the algorithm's different steps and minimize the latency bottleneck. We explore different variations of the DSA, including one that features the added benefit of ensuring constant timing across the different EBA steps, in order to mitigate the vulnerability to timing-based side-channel attacks. Our DSA improves the latency compared to a base ARM-based processor by up to 4.24×, while the constant timing DSA improves the latency by up to 19%. Also, our DSA improves the energy by up to 5.59×, as compared to the base processor.

Computer vision algorithms for dominant contact lens feature extraction using fuzzy-logic-based classifications

Iris image recognition is an emerging approach for human identification but offers low reliability. An algorithm for dominant contact lens feature extraction based on an improved neighboring binary pattern (NBP) approach is proposed herein. Features are compared with neighboring features in various directions, assigning a value of 1 to dominant features and 0 otherwise. The features in two-dimensional binary tables are then trained using an adaptive neuro fuzzy inference system (ANFIS) and classified using various classifiers. The performance of various feature descriptors based on the classification algorithms is measured and compared using parameters such as the accuracy, training time, positive acceptance rate (PAR), and negative acceptance rate (NAR), and the PAR and NAR are compared based upon a confusion matrix of classifiers. The proposed dominant feature extraction method achieves an accuracy rate of 95.7%.

Recognition, Classification for Normal, Contact and Cosmetic Iris Images using Deep Learning

The identification of the human being using iris based on image processing technique was one of better and older approach in human identification. Then to make human identification process an intelligent one using intelligent algorithms using machine learning techniques to train input images, extract features and classify the features based on classification techniques. The recent technology is enhancing for iris recognition based on deep learning networks, in which deeply train the images with number of layers, so that necessary features are extracted and then classify it and measure various parameters.

Comparing dental identifier charting in cone beam computed tomography scans and panoramic radiographs using INTERPOL coding for human identification

According to INTERPOL, the comparison of antemortem and postmortem dental identifiers is a scientifically reliable approach for human identification. This study aimed to quantify the prevalence of corresponding INTERPOL coded dental identifiers in cone beam computed tomography (CBCT) scans and panoramic radiographs (PR). The sample consisted of 100 CBCT scans and 100 PR taken the same day from the same patients (35 males and 65 females). Randomly and independently, forty-one INTERPOL coded dental identifiers were searched in each image. Wilcoxon test compared the prevalence of codes in CBCT scans and PR, Chi-square tested the dependence between codes and teeth; and multiple correspondence analyses (MCA) explored the association between codes and teeth in color maps for CBCT scans and PR. No statistically significant differences between the prevalence of identifiers in CBCT scans and PR were detected (p=0.693). In CBCT scans and PR, dependence between teeth and codes was detected (p<0.05). In the study sample, the strongest associations were found between the codes unerupted (UNE), partially erupted (ERU) and impacted (IMV) and third molars, both in CBCT scans and PR. INTERPOL coded dental identifiers registered on CBCT scans and PR can be exchanged during human identification.

Development and validation of a multiplex insertion/deletion marker panel, SifaInDel 45plex system

In addition to commonly used short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs), insertion and deletion polymorphisms (InDels) have considerable potential in the field of forensic genetics because they combine desirable characteristics of both STRs and SNPs. In the present study, the SifaInDel 45plex system was designed to amplify 45 InDel markers, including 27 autosomal InDels (A-InDels), 16 X chromosome InDels (X-InDels) and two Y chromosome InDels (Y-InDels), simultaneously in a single PCR procedure and then detect products by capillary electrophoresis (CE). We also optimized the PCR conditions for the novel panel and performed several validation studies including repeatability/reproducibility, concordance, accuracy, sensitivity, stability, species specificity and population genetics.The results confirmed that full profiles could be obtained from ≥62.5 pg of input DNA and from a series of challenging samples encountered in routine casework. The SifaInDel 45plex panel could tolerate different concentrations of inhibitors, such as ≤50 μM hematin, ≤20 ng/μL nigrosine and ≤8000 ng/μL urea. In a population investigation, for the 27 A-InDels, the combined power of discrimination (CPD) exceeded 0.999999, and the combined power of exclusion in duos (CPED) and trios (CPET) was 0.955118 and 0.997754, respectively. For the 16 X-InDels, the combined PDMale and PDFemale was computed as 0.999845 and 0.999998, respectively, and the combined mean exclusion chance in father/daughter or mother/son duos (MECDuo) and mean exclusion chance in standard trios involving daughters (MECTrio) was 0.976220 and 0.998163, respectively. In addition, the two Y-InDels could play a role in correctly determining gender. Overall, the established SifaInDel 45plex panel is a well-performing, reliable and robust multiplex system that stands out for combining a considerable number of A-indels, X-indels and Y-indels based on a CE platform. The population study results also demonstrated that the SifaInDel 45plex panel could be a valid complementary approach for human identification and complex kinship analysis.

HUMAN IDENTIFICATION ESTABLISHED BY THE ANALYSIS OF FRONTAL SINUS SEEN IN ANTEROPOSTERIOR SKULL RADIOGRAPHS USING THE MENTO-NASO TECHNIQUE – A FORENSIC CASE REPORT

Introduction: Human identification is a challenging task, especially when the available information detected during the dental autopsy is scarce. In this context, distinctive morphological information may play a valuable role as additional and alternative identifiers. Objective: To report a case of human identification aided by the morphological analysis of the frontal sinus through anteroposterior radiographs of the skull with the mento-naso technique. Material and methods: The body of an edentulous adult male highly decomposed was referred to the local medico-legal institute for identification. The alleged relatives of the victim provided the comparative antemortem data (AM) – consisting of an anteroposterior (AP) skull radiograph taken with mento-naso projection. A postmortem AP radiograph of the deceased was taken in order to reproduce the AM data and to enable a comparative procedure. Results : The morphological information of the frontal sinuses converged between AM and PM radiographs both for metric and non-metric evidences. Conclusion: The analysis of the frontal sinus may be an additional and alternative approach for human identification – especially for edentulous victims.

Fusion of sparse representation and dictionary matching for identification of humans in uncontrolled environment

Biomechanics based human identification is a major area of research. Biomechanics based approaches depend on accurately recognizing humans using body movements, the accuracy of these approaches is enhanced by incorporating the knee-hip angle to angle relationships. Current biomechanics based models are developed by considering the biomechanics of human walking and running. In biomechanics the joint angle characteristics, also known as gait features play a vital role in identification of humans. In general, identification of humans can be broadly classified into two approaches: biomechanics based approach, also known as Gait Recognition and biometric based Composite Sketch Matching. Gait recognition is a biomechanics based approach which uses gait traits for person authentication, it discriminates people by the way they walk.Gait recognition uses shape and motion information of a person and identifies the individual; this information is generally acquired from an image sequence. The efficiency of gait recognition is mainly affected by covariates such as observation view, walking speed, clothing, and belongings. Biometric based approach for human identification is usually done by composite sketch matching. Composite sketches are sketches generated using a computer. This obviates the need of using a skilled sketch artist; these sketches can be easily drawn by eyewitness using face design system software in a very short time period. This doesn’t require any prior specialized software training but identifying humans using only composite sketches is still a challenging task owing to the fact that human faces are not always clearly visible from a distance. Hence drawing a composite sketch at all times is not feasible.The key contribution of this paper is a fusion system developed by combining biomechanics based gait recognition and biometric based composite sketch matching for identifying humans in crowded scenes. First various existing biomechanics based approaches for gait recognitionare developed. Then a novel biomechanics based gait recognition is developed using Sparse Representation to generate what we term as “score 1.” Further another novel technique for composite sketch matching is developed using Dictionary Matching to generate what we term as “score 2.” Finally, score level fusion using Dempster Shafer and Proportional Conflict Distribution Rule Number 5 is performed. The proposed fusion approach is validated using a database containing biomechanics based gait sequences and biometric based composite sketches. From our analysis we find that a fusion of gait recognition and composite sketch matching provides excellent results for real-time human identification.

The art of traditional native PAGE: The APLP 48-ID assay for human identification.

When full STR profiles cannot be obtained, further DNA analyses targeting single nucleotide polymorphisms (SNPs) may occasionally yield valuable information. Although the discrimination power of each SNP is relatively low, combined analysis of many SNPs can improve the personal identification ability to a level as high as that of commercial STR typing kits. In this study, we developed a new SNP typing method, named the amplified-product length polymorphism (APLP) 48-ID assay, for genotyping of 47 autosomal SNPs and two X and Y chromosomal markers for sex typing. Forty-seven SNPs were selected from all 22 autosomes, showing high diversity in European, Nigerian, Han Chinese, and Japanese population in the HapMap data. PCR primers were designed to generate amplicons 40-100 bp in length to increase the robustness of the PCR. The APLP 48-ID assay consisted of four independent PCR reactions followed by electrophoretic run on four lanes in a polyacrylamide gel. Complete profiles were obtained when more than 1.2 ng of DNA was used. We applied this assay for genotyping of 236 Japanese individuals. The random matching probability was 3.3E-20, and the power of exclusion was greater than 0.9999999. This method is a rapid, robust, and cost-effective approach for human identification and paternity testing.

Fast medium-scale multiperson identification in aerial videos

Vision systems for unmanned aerial vehicles (UAVs) have been gaining increasing attention for surveillance and civil applications. However, aerial platforms create new challenges for several vision tasks (e.g., human tracking and identification) because UAV-mounted cameras undergo large vibration movements and capture unstable videos. Furthermore, most existing machine vision approaches use the fine details of a human figure, which are unavailable in low-quality aerial images. We propose a new blob-matching approach for human identification in aerial videos in which the identity of a human blob is estimated using an adaptive reference set of previously identified people. A target can be quickly located by matching only the target and a carefully selected candidate set. The experimental results obtained using several challenging aerial videos validated the effectiveness and computational efficiency of the proposed method.

A Novel Biometric Approach for Human Identification and Verification Using Eye Blinking Signal

In this letter, a novel technique is adopted for human recognition based on eye blinking waveform extracted from electro-oculogram signals. For this purpose, a database of 25 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted and applied for identification and verification tasks. The pre-processing stage includes empirical mode decomposition to isolate electro-oculogram signal from brainwaves. Then, time delineation of the eye blinking waveform is utilized for feature extraction. Finally, linear discriminant analysis is adopted for classification. Based on the achieved results, the proposed system can identify subjects with best accuracy of 97.3% and verify them with an equal error rate of 3.7%. The obtained results in this letter confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for human recognition task.

Retinal Identification System using Fourier-Mellin Transform and Fuzzy Clustering

The uniqueness of the retina blood vessels pattern is the most accurate pattern among other biometric systems. In this paper, we present a new approach for human identification using retina recognition and fuzzy C-means clustering algorithm. This method is insensitive to rotation, rescaling and transformation. The Fourier-Mellin transform coefficients and moments of the retinal image have been used as features extracted in our system. To compensate the rotational effects of the retinal scanner, a rotation compensator was designed. For optic disc localization, the Haar wavelet and snakes model have been used. The experimental results show an error rate close to zero for the propose method.

Gait Correlation Analysis Based Human Identification

Human gait identification aims to identify people by a sequence of walking images. Comparing with fingerprint or iris based identification, the most important advantage of gait identification is that it can be done at a distance. In this paper, silhouette correlation analysis based human identification approach is proposed. By background subtracting algorithm, the moving silhouette figure can be extracted from the walking images sequence. Every pixel in the silhouette has three dimensions: horizontal axis (x), vertical axis (y), and temporal axis (t). By moving every pixel in the silhouette image along these three dimensions, we can get a new silhouette. The correlation result between the original silhouette and the new one can be used as the raw feature of human gait. Discrete Fourier transform is used to extract features from this correlation result. Then, these features are normalized to minimize the affection of noise. Primary component analysis method is used to reduce the features' dimensions. Experiment based on CASIA database shows that this method has an encouraging recognition performance.