Non-Invasive Challenge Response Authentication for Voice Transactions with Smart Home Behavior

Smart speakers, such as Alexa and Google Home, support daily activities in smart home environments. Even though voice commands enable friction-less interactions, existing financial transaction authorization mechanisms hinder usability. A non-invasive authorization by leveraging presence and light sensors’ data is proposed in order to replace invasive procedure through smartphone notification. The Coloured Petri Net model was created for synthetic data generation, and one month data were collected in test bed with real users. Random Forest machine learning models were used for smart home behavior information retrieval. The LSTM prediction model was evaluated while using test bed data, and an open dataset from CASAS. The proposed authorization mechanism is based on Physical Unclonable Function usage as a random number generator seed in a Challenge Response protocol. The simulations indicate that the proposed scheme with specialized autonomous device could halve the total response time for low value financial transactions triggered by voice, from 7.3 to 3.5 s in a non-invasive manner, maintaining authorization security.

Coupled Attribute Learning for Heterogeneous Face Recognition

Heterogeneous face recognition (HFR) is a challenging problem in face recognition and subject to large textural and spatial structure differences of face images. Different from conventional face recognition in homogeneous environments, there exist many face images taken from different sources (including different sensors or different mechanisms) in reality. In addition, limited training samples of cross-modality pairs make HFR more challenging due to the complex generation procedure of these images. Despite the great progress that has been achieved in recent years, existing works mainly focus on HFR from only cross-modality image matching. However, it is more practical to obtain both facial images and semantic descriptions about facial attributes in real-world situations, in which the semantic description clues are nearly always obtained during the process of image generation. Motivated by human cognitive mechanisms, we naturally utilize the explicit invariant semantic description, i.e., face attributes, to help address the gap among face images of different modalities. Existing facial attributes-related face recognition methods primarily regard attributes as the high-level features used to enhance recognition performance, ignoring the inherent relationship between face attributes and identities. In this article, we propose novel coupled attribute learning for the HFR (CAL-HFR) method without labeling the attributes manually. Deep convolutional networks are employed to directly map face images in heterogeneous scenarios to a compact common space where distances are taken as dissimilarities of pairs. Coupled attribute guided triplet loss (CAGTL) is designed to train an end-to-end HFR network that can effectively eliminate defects of incorrectly estimated attributes. Extensive experiments on multiple heterogeneous scenarios demonstrate that the proposed method achieves superior performance compared with that of state-of-the-art methods. Furthermore, we make publicly available our generated pairwise annotated heterogeneous facial attribute database for evaluation and promoting related research.

Non-Segmented ECG bio-identification using Short Time Fourier Transform and Fréchet Mean Distance

In the recent years, the Electrocardiogram (ECG) based biometric identification has been a subject of considerable research interest. In this paper, we present non-fiducial method for ECG-identification using the short time Fourier transform (STFT), and Frechet mean distance-based algorithms to find the similarity between the STFTs of different people. In this study, we select randomly the training and test data of the ECG in order to test the stability of the method. We apply our proposed method on 124 ECG records of 62 subjects from the publicly available ECG ID database from physionet website. Our preliminary results indicate that the Frechet mean based ECG identification has 96.45% average identification accuracy and therefore can be potentially useful in various applications.

Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits

With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.

Detecting face presentation attacks in mobile devices with a patch-based CNN and a sensor-aware loss function

With the widespread use of biometric authentication comes the exploitation of presentation attacks, possibly undermining the effectiveness of these technologies in real-world setups. One example takes place when an impostor, aiming at unlocking someone else’s smartphone, deceives the built-in face recognition system by presenting a printed image of the user. In this work, we study the problem of automatically detecting presentation attacks against face authentication methods, considering the use-case of fast device unlocking and hardware constraints of mobile devices. To enrich the understanding of how a purely software-based method can be used to tackle the problem, we present a solely data-driven approach trained with multi-resolution patches and a multi-objective loss function crafted specifically to the problem. We provide a careful analysis that considers several user-disjoint and cross-factor protocols, highlighting some of the problems with current datasets and approaches. Such analysis, besides demonstrating the competitive results yielded by the proposed method, provides a better conceptual understanding of the problem. To further enhance efficacy and discriminability, we propose a method that leverages the available gallery of user data in the device and adapts the method decision-making process to the user’s and the device’s own characteristics. Finally, we introduce a new presentation-attack dataset tailored to the mobile-device setup, with real-world variations in lighting, including outdoors and low-light sessions, in contrast to existing public datasets.

Why Temporal Persistence of Biometric Features, as Assessed by the Intraclass Correlation Coefficient, Is So Valuable for Classification Performance

It is generally accepted that relatively more permanent (i.e., more temporally persistent) traits are more valuable for biometric performance than less permanent traits. Although this finding is intuitive, there is no current work identifying exactly where in the biometric analysis temporal persistence makes a difference. In this paper, we answer this question. In a recent report, we introduced the intraclass correlation coefficient (ICC) as an index of temporal persistence for such features. Here, we present a novel approach using synthetic features to study which aspects of a biometric identification study are influenced by the temporal persistence of features. What we show is that using more temporally persistent features produces effects on the similarity score distributions that explain why this quality is so key to biometric performance. The results identified with the synthetic data are largely reinforced by an analysis of two datasets, one based on eye-movements and one based on gait. There was one difference between the synthetic and real data, related to the intercorrelation of features in real data. Removing these intercorrelations for real datasets with a decorrelation step produced results which were very similar to that obtained with synthetic features.

Focus on the Visible Regions: Semantic-Guided Alignment Model for Occluded Person Re-Identification

The occlusion problem is very common in pedestrian retrieval scenarios. When persons are occluded by various obstacles, the noise caused by the occluded area greatly affects the retrieval results. However, many previous pedestrian re-identification (Re-ID) methods ignore this problem. To solve it, we propose a semantic-guided alignment model that uses image semantic information to separate useful information from occlusion noise. In the image preprocessing phase, we use a human semantic parsing network to generate probability maps. These maps show which regions of images are occluded, and the model automatically crops images to preserve the visible parts. In the construction phase, we fuse the probability maps with the global features of the image, and semantic information guides the model to focus on visible human regions and extract local features. During the matching process, we propose a measurement strategy that only calculates the distance of public areas (visible human areas on both images) between images, thereby suppressing the spatial misalignment caused by non-public areas. Experimental results on a series of public datasets confirm that our method outperforms previous occluded Re-ID methods, and it achieves top performance in the holistic Re-ID problem.

Data Improvement Model Based on ECG Biometric for User Authentication and Identification

The rapid spread of wearable technologies has motivated the collection of a variety of signals, such as pulse rate, electrocardiogram (ECG), electroencephalogram (EEG), and others. As those devices are used to do so many tasks and store a significant amount of personal data, the concern of how our data can be exposed starts to gain attention as the wearable devices can become an attack vector or a security breach. In this context, biometric also has expanded its use to meet new security requirements of authentication demanded by online applications, and it has been used in identification systems by a large number of people. Existing works on ECG for user authentication do not consider a population size close to a real application. Finding real data that has a big number of people ECG’s data is a challenge. This work investigates a set of steps that can improve the results when working with a higher number of target classes in a biometric identification scenario. These steps, such as increasing the number of examples, removing outliers, and including a few additional features, are proven to increase the performance in a large data set. We propose a data improvement model for ECG biometric identification (user identification based on electrocardiogram—DETECT), which improves the performance of the biometric system considering a greater number of subjects, which is closer to a security system in the real world. The DETECT model increases precision from 78% to 92% within 1500 subjects, and from 90% to 95% within 100 subjects. Moreover, good False Rejection Rate (i.e., 0.064003) and False Acceptance Rate (i.e., 0.000033) were demonstrated. We designed our proposed method over PhysioNet Computing in Cardiology 2018 database.

The Security-Utility Trade-off for Iris Authentication and Eye Animation for Social Virtual Avatars

The gaze behavior of virtual avatars is critical to social presence and perceived eye contact during social interactions in Virtual Reality. Virtual Reality headsets are being designed with integrated eye tracking to enable compelling virtual social interactions. This paper shows that the near infra-red cameras used in eye tracking capture eye images that contain iris patterns of the user. Because iris patterns are a gold standard biometric, the current technology places the user's biometric identity at risk. Our first contribution is an optical defocus based hardware solution to remove the iris biometric from the stream of eye tracking images. We characterize the performance of this solution with different internal parameters. Our second contribution is a psychophysical experiment with a same-different task that investigates the sensitivity of users to a virtual avatar's eye movements when this solution is applied. By deriving detection threshold values, our findings provide a range of defocus parameters where the change in eye movements would go unnoticed in a conversational setting. Our third contribution is a perceptual study to determine the impact of defocus parameters on the perceived eye contact, attentiveness, naturalness, and truthfulness of the avatar. Thus, if a user wishes to protect their iris biometric, our approach provides a solution that balances biometric protection while preventing their conversation partner from perceiving a difference in the user's virtual avatar. This work is the first to develop secure eye tracking configurations for VR/AR/XR applications and motivates future work in the area.

ECG Biometric Recognition: Template-Free Approaches Based on Deep Learning

Biometric technologies offer much convenience over the conventional approaches to identity recognition, but security and privacy concerns also accompany their applications. In this paper, an electrocardiogram (ECG)-based identification scheme is proposed to relieve such concerns. With the help of a deep learning (DL) technique, the identity of an unknown beat bundle can be determined without the need for biometric template construction. Thus, the disclosure of the physiological and pathological condition of an individual from his/her stolen templates will no longer be possible. Furthermore, the problem of being vulnerable to unregistered subjects in this DL-based recognition system is also addressed. Experiments with real and synthesized ECGs are used to illustrate the efficacy of the proposed scheme. An identification rate of 97.84% for the 200 registered subjects with a false-positive identification rate of 0.69% under the attack of 1,000 synthesized single-lead ECGs was achieved.

Attention Based CNN-ConvLSTM for Pedestrian Attribute Recognition

As a result of its important role in video surveillance, pedestrian attribute recognition has become an attractive facet of computer vision research. Because of the changes in viewpoints, illumination, resolution and occlusion, the task is very challenging. In order to resolve the issue of unsatisfactory performance of existing pedestrian attribute recognition methods resulting from ignoring the correlation between pedestrian attributes and spatial information, in this paper, the task is regarded as a spatiotemporal, sequential, multi-label image classification problem. An attention-based neural network consisting of convolutional neural networks (CNN), channel attention (CAtt) and convolutional long short-term memory (ConvLSTM) is proposed (CNN-CAtt-ConvLSTM). Firstly, the salient and correlated visual features of pedestrian attributes are extracted by pre-trained CNN and CAtt. Then, ConvLSTM is used to further extract spatial information and correlations from pedestrian attributes. Finally, pedestrian attributes are predicted with optimized sequences based on attribute image area size and importance. Extensive experiments are carried out on two common pedestrian attribute datasets, PEdesTrian Attribute (PETA) dataset and Richly Annotated Pedestrian (RAP) dataset, and higher performance than other state-of-the-art (SOTA) methods is achieved, which proves the superiority and validity of our method.

Phase-domain Deep Patient-ECG Image Learning for Zero-effort Smart Health Security

Smart health is quickly boosted by technological advancements: smart sensors, body sensor network, internet of medical things and big data. Vast amounts of smart health big data from ubiquitous sensors pose unprecedented challenges to the security and privacy protection, which is extremely critical in healthcare applications. The vital signs, user daily behaviors, medicine recommendations, and so many other health data are vulnerable to different attacks, due to the fact that wearable/mobile monitors have very strict performance/power constraints, which limit the complexity of security protocols. In this paper, we study how to leverage a natural vital signal (Electrocardiogram - ECG) for user identification purpose, without introducing new hardware sensing devices. ECG is not only a gold standard cardiac signal, but also unique to each individual. We investigate a phase-domain deep patient-ECG image learning framework, to tackle key challenges in ECG biometric user identification: high diversities of ECG morphologies due to heart diseases, and time-consuming/ineffective heartbeat localization methods & manual feature engineering. The ultimate goal is to make the smart health security zero-effort: use `phase-domain transformation' to enable blind signal segmentation without localizing heartbeats; create a computer image processing-like task by `pixelating' phase-domain ECG trajectories to ECG images; and enable automatic (non-manual) `deep feature learning' using a deep convolutional neural network. Evaluated on two patient-ECG databases, this zero-effort framework achieves an accuracy as high as 97.2%, and greatly outperforms state-of-the-art studies in terms of the generalization ability and/or performance. This study is expected to enable highly challenging patient-ECG biometric user identification, by generalizable blind signal segmentation and deep feature learning strategies, in the era of smart health boosted by internet of medical things and big medical data.

Deep Neural Network-Based Gait Classification Using Wearable Inertial Sensor Data

Human gait has been regarded as a useful behavioral biometric trait for personal identification and authentication. This study aimed to propose an effective approach for classifying gait, measured using wearable inertial sensors, based on neural networks. The 3-axis accelerometer and 3-axis gyroscope data were acquired at the posterior pelvis, both thighs, both shanks, and both feet while 29 semi-professional athletes, 19 participants with normal foot, and 21 patients with foot deformities walked on the 20-meter straight path. The classifier based on the gait parameters and fully connected neural network was developed by applying 4-fold cross-validation to 80% of the total samples. For the test set that consisted of the remaining 20% of the total samples, this classifier showed an accuracy of 93.02% in categorizing the athlete, normal foot, and deformed foot groups. Using the same model validation and evaluation method, up to 98.19% accuracy was achieved from the convolutional neural network-based classifier. This classifier was trained with the gait spectrograms obtained from the time-frequency domain analysis of the raw acceleration and angular velocity data. The classification based only on the pelvic spectrograms exhibited an accuracy of 94.25% even without requiring a time-consuming and resource-intensive process for feature engineering. The notable performance and practicality in gait classification achieved by this study suggest potential applicability of the proposed approaches in the field of biometrics.

Unsupervised face anti-spoofing using dual cameras based feature matching

Face anti-spoofing is a crucial part of face recognition system to protect subject's privacy and life safety. Most current face anti-spoofing algorithms are based on feature extraction and machine learning. The performance of machine learning based approaches depends on the quantity and quality of the training data. In this paper, we propose an unsupervised face anti-spoofing method based on feature extraction and matching of a dual camera setup, which does not require offline training. The principle of our method is simple, intuitive, and generally applicable. The core idea of our method is exploiting the fact that a 3D face has different feature representations in images from two cameras with different view angles, as compared to that of a 2D spoofing face (either printed in a paper or showing on a screen). The proposed method has been benchmarked on a dataset created by our dual camera setup and shows an accuracy of 94.2%.

In-field Remote Fingerprint Authentication using Human Body Communication and On-Hub Analytics

In this emerging data-driven world, secure and ubiquitous authentication mechanisms are necessary prior to any confidential information delivery. Biometric authentication has been widely adopted as it provides a unique and non-transferable solution for user authentication. In this article, the authors envision the need for an infield, remote and on-demand authentication system for a highly mobile and tactical environment, such as critical information delivery to soldiers in a battlefield. Fingerprint-based in-field biometric authentication combined with the conventional password-based techniques would ensure strong security of critical information delivery. The proposed in-field fingerprint authentication system involves: (i) wearable fingerprint sensor, (ii) template extraction (TE) algorithm, (iii) data encryption, (iv) on-body and long-range communications, all of which are subject to energy constraints due to the requirement of small form-factor wearable devices. This paper explores the design space and provides an optimized solution for resource allocation to enable energy-efficient in-field fingerprint- based authentication. Using Human Body Communication (HBC) for the on-body data transfer along with the analytics (TE algorithm) on the hub allows for the maximum lifetime of the energy-sparse sensor. A custom-built hardware prototype using COTS components demonstrates the feasibility of the in-field fingerprint authentication framework.

Thermophysical Fingerprinting of Probiotic-Based Products

Variability in efficacy and safety is a worldwide concern with commercial probiotics for their growing and inevitable use in food and health sectors. Here, we introduce a probiotic thermophysical fingerprinting methodology using a coupling thermogravimetry and differential scanning calorimetry. Qualitative and quantitative information on the material decomposition and transition phases is provided under heating conditions. By monitoring the changes in both mass and internal energy over temperature and time, a couple of thermal data at the maximum decomposition steps allow the creation of a unique and global product identity, depending on both strain and excipient components. We demonstrate that each powder formulation of monostrain and multistrain from different lots and origins have a unique thermophysical profile. Our approach also provides information on the formulation thermostability and additive/excipient composition. An original fingerprint form is proposed by converting the generated thermal data sequence into a star-like pattern for a perspective library construction.

Facial recognition from DNA using face-to-DNA classifiers

Facial recognition from DNA refers to the identification or verification of unidentified biological material against facial images with known identity. One approach to establish the identity of unidentified biological material is to predict the face from DNA, and subsequently to match against facial images. However, DNA phenotyping of the human face remains challenging. Here, another proof of concept to biometric authentication is established by using multiple face-to-DNA classifiers, each classifying given faces by a DNA-encoded aspect (sex, genomic background, individual genetic loci), or by a DNA-inferred aspect (BMI, age). Face-to-DNA classifiers on distinct DNA aspects are fused into one matching score for any given face against DNA. In a globally diverse, and subsequently in a homogeneous cohort, we demonstrate preliminary, but substantial true (83%, 80%) over false (17%, 20%) matching in verification mode. Consequences of future efforts include forensic applications, necessitating careful consideration of ethical and legal implications for privacy in genomic databases.

CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment

Advancements in wireless sensor network technologies have enabled the proliferation of miniaturized body-worn sensors, capable of long-term pervasive biomedical signal monitoring. Remote cardiovascular monitoring has been one of the beneficiaries of this development, resulting in non-invasive, photoplethysmography (PPG) sensors being used in ambulatory settings. Wrist-worn PPG, although a popular alternative to electrocardiogram, suffers from motion artifacts inherent in daily life. Hence, in this paper, we present a novel deep learning framework (CorNET) to efficiently estimate heart rate (HR) information and perform biometric identification (BId) using only a wrist-worn, single-channel PPG signal collected in ambulant environment. We have formulated a completely personalized data-driven approach, using a four-layer deep neural network. Two convolution neural network layers are used in conjunction with two long short-term memory layers, followed by a dense output layer for modeling the temporal sequence inherent within the pulsatile signal representative of cardiac activity. The final dense layer is customized with respect to the application, functioning as: regression layer-having a single neuron to predict HR; classification layer-two neurons that identify a subject among a group. The proposed network was evaluated on the TROIKA dataset having 22 PPG records collected during various physical activities. We achieve a mean absolute error of 1.47 ± 3.37 beats per minute for HR estimation and an average accuracy of 96% for BId on 20 subjects. CorNET was further evaluated successfully in an ambulant use-case scenario with custom sensors for two subjects.

Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning

IMPORTANCE

Despite data aggregation and removal of protected health information, there is concern that deidentified physical activity (PA) data collected from wearable devices can be reidentified. Organizations collecting or distributing such data suggest that the aforementioned measures are sufficient to ensure privacy. However, no studies, to our knowledge, have been published that demonstrate the possibility or impossibility of reidentifying such activity data.

OBJECTIVE

To evaluate the feasibility of reidentifying accelerometer-measured PA data, which have had geographic and protected health information removed, using support vector machines (SVMs) and random forest methods from machine learning.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study, the National Health and Nutrition Examination Survey (NHANES) 2003-2004 and 2005-2006 data sets were analyzed in 2018. The accelerometer-measured PA data were collected in a free-living setting for 7 continuous days. NHANES uses a multistage probability sampling design to select a sample that is representative of the civilian noninstitutionalized household (both adult and children) population of the United States.

EXPOSURES

The NHANES data sets contain objectively measured movement intensity as recorded by accelerometers worn during all walking for 1 week.

MAIN OUTCOMES AND MEASURES

The primary outcome was the ability of the random forest and linear SVM algorithms to match demographic and 20-minute aggregated PA data to individual-specific record numbers, and the percentage of correct matches by each machine learning algorithm was the measure.

RESULTS

A total of 4720 adults (mean [SD] age, 40.0 [20.6] years) and 2427 children (mean [SD] age, 12.3 [3.4] years) in NHANES 2003-2004 and 4765 adults (mean [SD] age, 45.2 [19.9] years) and 2539 children (mean [SD] age, 12.1 [3.4] years) in NHANES 2005-2006 were included in the study. The random forest algorithm successfully reidentified the demographic and 20-minute aggregated PA data of 4478 adults (94.9%) and 2120 children (87.4%) in NHANES 2003-2004 and 4470 adults (93.8%) and 2172 children (85.5%) in NHANES 2005-2006 (P < .001 for all). The linear SVM algorithm successfully reidentified the demographic and 20-minute aggregated PA data of 4043 adults (85.6%) and 1695 children (69.8%) in NHANES 2003-2004 and 4041 adults (84.8%) and 1705 children (67.2%) in NHANES 2005-2006 (P < .001 for all).

CONCLUSIONS AND RELEVANCE

This study suggests that current practices for deidentification of accelerometer-measured PA data might be insufficient to ensure privacy. This finding has important policy implications because it appears to show the need for deidentification that aggregates the PA data of multiple individuals to ensure privacy for single individuals.

An efficient dynamic ID-based remote user authentication scheme using self-certified public keys for multi-server environments

Recently, Li et al. proposed a novel smart card and dynamic ID-based remote user authentication scheme for multi-server environments. They claimed that their scheme can resist several types of attacks. However, through careful analysis, we find that Li et al.’s scheme is vulnerable to stolen smart card and off-line dictionary attacks, replay attacks, impersonation attacks and server spoofing attacks. By analyzing other similar schemes, we find that a certain type of dynamic ID-based multi-server authentication scheme in which only hash functions are used and whereby no registration center participates in the authentication and session key agreement phase faces difficulties in providing perfectly efficient and secure authentication. To compensate for these shortcomings, we propose a novel dynamic ID-based remote user authentication scheme for multi-server environments based on pairing and self-certified public keys. Security and performance analyses show that the proposed scheme is secure against various attacks and has many excellent features.

Security and efficiency enhancement of an anonymous three-party password-authenticated key agreement using extended chaotic maps

Recently, Lu et al. claimed that Xie et al.’s three-party password-authenticated key agreement protocol (3PAKA) using chaotic maps has three security vulnerabilities; in particular, it cannot resist offline password guessing attack, Bergamo et al.’s attack and impersonation attack, and then they proposed an improved protocol. However, we demonstrate that Lu et al.’s attacks on Xie et al.’s scheme are unworkable, and their improved protocol is insecure against stolen-verifier attack and off-line password guessing attack. Furthermore, we propose a novel scheme with enhanced security and efficiency. We use formal verification tool ProVerif, which is based on pi calculus, to prove security and authentication of our scheme. The efficiency of the proposed scheme is higher than other related schemes.

Characteristics of fatal road crashes involving unlicensed drivers or riders: Implications for countermeasures

Drivers or riders without a valid license are involved in 10% of fatal road crashes in Norway. This was shown by an analysis of data from all fatal crashes in the period 2005-2014. A literature review shows that unlicensed drivers have a considerably increased crash risk. Such crashes could be prevented by electronic driver authentication, i.e., a technical system for checking that a driver or rider has legal access to a vehicle before driving is permitted. This can be done by requiring the driver/rider to identify themselves with a national identity number and a unique code or biometric information before driving may commence. The vehicle thereafter verifies license availability and vehicle access by communication with a central register. In more than 80% of fatal crashes with unlicensed drivers/riders, speeding and/or drug influence contributed to the crash. This means that a majority of crashes with unlicensed drivers alternatively could be prevented by already available systems, such as alcolock and speed limit dependent speed adapters. These systems will have a wider influence, by preventing crashes also among licensed drivers. Mandatory implementation of alcolock, speed limiter, and electronic driver authentication in all motorized vehicles is estimated to prevent up to 28% of fatal road crashes, depending on effectiveness of the systems.

Learning from Ambiguously Labeled Face Images

Learning a classifier from ambiguously labeled face images is challenging since training images are not always explicitly-labeled. For instance, face images of two persons in a news photo are not explicitly labeled by their names in the caption. We propose a Matrix Completion for Ambiguity Resolution (MCar) method for predicting the actual labels from ambiguously labeled images. This step is followed by learning a standard supervised classifier from the disambiguated labels to classify new images. To prevent the majority labels from dominating the result of MCar, we generalize MCar to a weighted MCar (WMCar) that handles label imbalance. Since WMCar outputs a soft labeling vector of reduced ambiguity for each instance, we can iteratively refine it by feeding it as the input to WMCar. Nevertheless, such an iterative implementation can be affected by the noisy soft labeling vectors, and thus the performance may degrade. Our proposed Iterative Candidate Elimination (ICE) procedure makes the iterative ambiguity resolution possible by gradually eliminating a portion of least likely candidates in ambiguously labeled faces. We further extend MCar to incorporate the labeling constraints among instances when such prior knowledge is available. Compared to existing methods, our approach demonstrates improvements on several ambiguously labeled datasets.

Blinking characterization from high speed video records. Application to biometric authentication

The evaluation of eye blinking has been used for the diagnosis of neurological disorders and fatigue. Despite the extensive literature, no objective method has been found to analyze its kinematic and dynamic behavior. A non-contact technique based on the high-speed recording of the light reflected by the eyelid in the blinking process and the off-line processing of the sequence is presented. It allows for objectively determining the start and end of a blink, besides obtaining different physical magnitudes: position, speed, eyelid acceleration as well as the power, work and mechanical impulse developed by the muscles involved in the physiological process. The parameters derived from these magnitudes provide a unique set of features that can be used to biometric authentication. This possibility has been tested with a limited number of subjects with a correct identification rate of up to 99.7%, thus showing the potential application of the method.

Personal Identification of Deceased Persons: An Overview of the Current Methods Based on Physical Appearance

The use of the physical appearance of the deceased has become more important because the available antemortem information for comparisons may consist only of a physical description and photographs. Twenty-one articles dealing with the identification based on the physiognomic features of the human body were selected for review and were divided into four sections: (i) visual recognition, (ii) specific facial/body areas, (iii) biometrics, and (iv) dental superimposition. While opinions about the reliability of the visual recognition differ, the search showed that it has been used in mass disasters, even without testing its objectivity and reliability. Specific facial areas being explored for the identification of dead; however, their practical use is questioned, similarly to soft biometrics. The emerging dental superimposition seems to be the only standardized and successfully applied method for identification so far. More research is needed into a potential use of the individualizing features, considering that postmortem changes and technical difficulties may affect the identification.