DriverSonar

Dangerous driving due to drowsiness and distraction is the main cause of traffic accidents, resulting in casualties and economic loss. There is an urgent need to address this problem by accurately detecting dangerous driving behaviors and generating real-time alerts. Inspired by the observation that dangerous driving actions induce unique acoustic features that respond to the signal of an acoustic source, we present the DriverSonar system in this paper. The proposed system detects dangerous driving actions and generates real-time alarms using off-the-shelf smartphones. Compared with the state-of-the-arts, the DriverSonar system does not require dedicated sensors but just uses the built-in speaker and microphone in a smartphone. Specifically, DriverSonar is able to recognize head/hand motions such as nodding, yawning, and abrupt adjustment of the steering wheel. We design, implement and evaluate DriverSonar with extensive experiments. We conduct both simulator-based and and real driving-based experiments (IRB-approved) with 30 volunteers for a period over 12 months. Experiment results show that the proposed system can detect drowsy and distraction related dangerous driving actions at an precision up to 93.2% and a low false acceptance rate of 3.6%.

Local gradient pattern and deep learning-based approach for the iris recognition at-a-distance

One of the biometric techniques utilized to predict the human is based on the iris. The recognition of iris is performed by discovering an individual without human intervention utilizing the iris of human eyes. Iris offers distinct information about the person. This research presents deep learning strategy for performing iris recognition. Primarily, image is pre-processed to obtain exact region iris. Then, region of iris is extracted using Hough Transform, followed by segmentation and normalization of iris region using Daugman’s rubber sheet model. Once segmentation is performed, the features are generated with ScaT-LOOP that is the combination of Scattering Transform (ST), Tetrolet transforms (TT), Local Gradient Pattern (LGP) and Local Optimal Oriented Pattern (LOOP). Finally, steepest gradient-based Deep Belief Network (DBN) is utilized for recognizing the iris. The performance of iris recognition using the DBN classifier is computed based on accuracy, False Rejection Rate (FRR), and False Acceptance Rate (FAR). The proposed method achieves maximum accuracy of 97.96%, minimal FAR of 0.493%, and minimal FRR of 0.48% that indicates its superiority.

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

ScatT-LOOP: scattering tetrolet-LOOP descriptor and optimized NN for iris recognition at-a-distance

Iris Recognition at-a Distance (IAAD) is a major challenge for researchers due to the defects associated with the visual imaging and poor image quality in dynamic environments, which imposed bad impacts on the accuracy of recognition. Thus, in order to enable the effective IAAD, this paper proposes a new method, named, Chronological Monarch Butterfly Optimization (Chronological MBO)-enabled Neural Network (NN). The recognition of iris using NN is trained with the proposed Chronological MBO, which is developed through the combination of Chronological theory in Monarch Butterfly Optimization (MBO). The recognition becomes effective with the automatic segmentation and the normalization of iris image on the basis of Hough Transform (HT) and Daugman’s rubber sheet model followed with the process of feature extraction with the developed ScatT-LOOP descriptor, which is the integration of scattering transform (ST), Local Optimal Oriented Pattern (LOOP) descriptor, and Tetrolet transform (TT). The developed ScatT-LOOP descriptor extracts the texture as well as the orientation details of image for effective recognition. The analysis is evaluated with the CASIA Iris dataset with respect to the evaluation metrics, accuracy, False Acceptance Rate (FAR), and False Rejection Rate (FRR). The proposed method has the accuracy, FRR, and FAR of 0.97, 0.005, and 0.005, respectively. The experimental results proved that the proposed method is effective than the existing methods of iris recognition.

Robustness of Rhythmic-Based Dynamic Hand Gesture with Surface Electromyography (sEMG) for Authentication

Authentication has three basic factors—knowledge, ownership, and inherence. Biometrics is considered as the inherence factor and is widely used for authentication due to its conveniences. Biometrics consists of static biometrics (physical characteristics) and dynamic biometrics (behavioral). There is a trade-off between robustness and security. Static biometrics, such as fingerprint and face recognition, are often reliable as they are known to be more robust, but once stolen, it is difficult to reset. On the other hand, dynamic biometrics are usually considered to be more secure due to the constant changes in behavior but at the cost of robustness. In this paper, we proposed a multi-factor authentication—rhythmic-based dynamic hand gesture, where the rhythmic pattern is the knowledge factor and the gesture behavior is the inherence factor, and we evaluate the robustness of the proposed method. Our proposal can be easily applied with other input methods because rhythmic pattern can be observed, such as during typing. It is also expected to improve the robustness of the gesture behavior as the rhythmic pattern acts as a symbolic cue for the gesture. The results shown that our method is able to authenticate a genuine user at the highest accuracy of 0.9301 ± 0.0280 and, also, when being mimicked by impostors, the false acceptance rate (FAR) is as low as 0.1038 ± 0.0179.

Taylor-DBN: A new framework for speech recognition systems

Speech recognition is a rapidly emerging research area as the speech signal contains linguistic information and speaker information that can be used in applications including surveillance, authentication, and forensic field. The performance of speech recognition systems degrades expeditiously nowadays due to channel degradations, mismatches, and noise. To provide better performance of speech recognition, the Taylor-Deep Belief Network (Taylor-DBN) classifier is proposed, which is the modification of the Gradient Descent (GD) algorithm with Taylor series in the existing DBN classifier. Initially, the noise present in the speech signal is removed through the speech signal enhancement. The features, such as Holoentropy with the eXtended Linear Prediction using autocorrelation Snapshot (HXLPS), spectral kurtosis, and spectral skewness, are extracted from the enhanced speech signal, which is fed to the Taylor-DBN classifier that identifies the speech of the impaired persons. The experimentation is done using the TensorFlow speech recognition database, the real database, and the ESC-50 dataset. The accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and Mean Square Error (MSE) of the Taylor-DBN for TensorFlow speech recognition database are 96.95%, 3.04%, 3.04%, and 0.045, respectively, and for real database, the accuracy, FAR, FRR, and MSE are 96.67%, 3.32%, 3.32%, and 0.0499, respectively. Similarly, for the ESC-50 dataset, the accuracy, FAR, FRR, and MSE are 96.81%, 3.18%, 3.18%, and 0.047, respectively. The results imply that the Taylor-DBN provides better performance as compared to the existing conventional methods.

Robust Single-Trial EEG-Based Authentication Achieved with a 2-Stage Classifier

The risk of personal data exposure through unauthorized access has never been as imminent as today. To counter this, biometric authentication has been proposed: the use of distinctive physiological and behavioral characteristics as a form of identification and access control. One of the recent developments is electroencephalography (EEG)-based authentication. It builds on the subject-specific nature of brain responses which are difficult to recreate artificially. We propose an authentication system based on EEG signals recorded in response to a simple motor paradigm. Authentication is achieved with a novel two-stage decoder. In the first stage, EEG signal features are extracted using an inception- and a VGG-like deep learning neural network (NN) both of which we compare with principal component analysis (PCA). In the second stage, a support vector machine (SVM) is used for binary classification to authenticate the subject based on the extracted features. All decoders are trained on EEG motor-movement data recorded from 105 subjects. We achieved with the VGG-like NN-SVM decoder a false-acceptance rate (FAR) of 2.55% with an overall accuracy of 88.29%, a FAR of 3.33% with an accuracy of 87.47%, and a FAR of 2.89% with an accuracy of 90.68% for 8, 16, and 64 channels, respectively. With the Inception-like NN-SVM decoder we achieved a false-acceptance rate (FAR) of 4.08% with an overall accuracy of 87.29%, a FAR of 3.53% with an accuracy of 85.31%, and a FAR of 1.27% with an accuracy of 93.40% for 8, 16, and 64 channels, respectively. The PCA-SVM decoder achieved accuracies of 92.09%, 92.36%, and 95.64% with FARs of 2.19%, 2.17%, and 1.26% for 8, 16, and 64 channels, respectively.

Intelligent Authentication Model in a Hierarchical Wireless Sensor Network With Multiple Sinks

A wireless sensor network consists of a number of sensors laid out in a field with mobile sinks dynamically aggregating data from the nodes. Sensitive applications such as military environment require the sink to identify if a sensor that it visits is legitimate, and in turn, the sensor has to ensure that the sink is authenticated to access its sensitive data. For the system to intelligently learn the credentials of non-malicious sink and non-malicious sensors based on the dynamically observed data, four approaches using access control lists, authenticator tokens, message digests, and elliptic curve variant of RSA algorithm are proposed along with the formal logic for correctness. The experimented data is analysed using false acceptance rate, false rejection rate, precision, and curve analysis parameters. The approaches are further compared based on the attacks they are vulnerable to and execution time, ultimately concluding that exchange of message digests and elliptic curve RSA algorithm are more widely applicable.

Multimodal score level fusion for recognition using face and palmprint

The paper proposes a t-norm-based matching score fusion approach for a multimodal heterogenous biometric recognition system. Two trait-based multimodal recognition system is developed by using biometrics traits like palmprint and face. First, palmprint and face are pre-processed, extracted features and calculated matching score of each trait using correlation coefficient and combine matching scores using t-norm based score level fusion. Face database like Face 94, Face 95, Face 96, FERET, FRGC and palmprint database like IITD are operated for training and testing of algorithm. The results of experimentation show that the proposed algorithm provides the Genuine Acceptance Rate (GAR) of 99.7% at False Acceptance Rate (FAR) of 0.1% and GAR of 99.2% at FAR of 0.01% significantly improves the accuracy of a biometric recognition system. The proposed algorithm provides the 0.53% more accuracy at FAR of 0.1% and 2.77% more accuracy at FAR of 0.01%, when compared to existing works.

Detection of Speaker De-identification Disguise Based on Dense Convolutional Network

Nowadays, speaker disguise is a common operation that presents a great challenge to social security. Therefore, it is important to recognize the authenticity of speech. Most of the current researches focus on speech spoofing, which simulates a target speaker to break through the state-of-art ASV systems by increasing false acceptance rate. Meanwhile, there is another type of disguise, i.e. de-identification, which transforms a speech signal without a target to increase the false rejection rate in order not to be recognized. It has received far less attention. Therefore, in this paper, we investigate the de-identification model and propose a method to detect de-identification speeches from genuine speeches by using a very deep dense convolutional network with 135 layers. The experimental results show that the average accuracy of the proposed method outperforms the reported state-of-the-art methods.