Due to its small size, slow flying speed, and low flying altitude, classification of mini-sized unmanned aerial vehicles (UAVs) using a frequency-modulated continuous wave (FMCW) surveillance radar is a challenging task. This is because the FMCW radar echo signals are acquired at a short dwell time and thus contain limited information about targets. In this paper, we first analyze FMCW radar returns from various types of UAVs and non-UAV objects in terms of the micro-Doppler signature (m-DS) pattern. Based on the analysis results, we propose an effective and efficient UAV classification system using FMCW radar echo signals. The proposed system consists of five main parts namely, (i) burst selection, (ii) rule-based scan pruning, (iii) the empirical mode decomposition based m-DS analysis and features extraction, (iv) error counting minimization based class label estimation, and (v) scan-to-scan filtering. Our experimental results on physically measured FMCW radar echo signals from several types of UAVs and non-UAV objects show that the proposed system consistently outperforms a commercial-off-the-shelf UAV classification system in terms of the classification accuracy .

In this study, the authors propose an efficient extension of the standard empirical mode decomposition (EMD) for complex‐valued univariate signal decomposition. The key idea of the extension is to convert a complex‐valued univariate signal into a longer real‐valued signal by augmenting the real part with the flipped imaginary part, and then to decompose it into intrinsic mode functions (IMFs) using the EMD once only. The bivariate IMFs are then retrieved from the obtained IMFs. Their empirical results on synthetic data show that the proposed method significantly outperforms the traditional bivariate EMD (BEMD) method in terms of computational efficiency while producing a comparable extraction error. Moreover, the proposed method shows better micro‐Doppler signature analysis performance on physically measured continuous‐wave radar data than that of the BEMD.

In this paper, we first investigate into six popular entropies extracted from a set of intrinsic mode functions (IMFs) as a feature pattern for radar-based mini-size unmanned aerial vehicles (mini-UAV) classification. The six entropies include Shannon entropy, spectral entropy, log energy entropy, approximate entropy, fuzzy entropy and permutation entropy. Via an empirical comparison among the six entropies on real measurement radar data, the first three are selected as the representative due to their high efficiency and accuracy. To enhance the classification accuracy, the three selected entropies are then extracted from eight different sets of IMFs obtained by signal downsampling, and then fused at feature level. The nonlinear support vector machine classifier is adopted to predict the class label of unseen test radar signals. Our empirical results on a set of real-world continuous wave radar data show that the proposed method outperforms the state-of-the-art method in terms of the mini-UAV classification accuracy.

In this letter, we propose an empirical-mode decomposition (EMD)-based method for automatic multicategory mini-unmanned aerial vehicle (UAV) classification. The radar echo signal is first decomposed into a set of oscillating waveforms by EMD. Then, eight statistical and geometrical features are extracted from the oscillating waveforms to capture the phenomenon of blade flashes. After feature normalization and fusion, a nonlinear support vector machine is trained for target class-label prediction. Our empirical results on real measurement of radar signals show encouraging mini-UAV classification accuracy performance.

Gabor magnitude is known to be among the most discriminative representations for face images due to its space- frequency co-localization property. However, such property causes adverse effects even when the images are acquired under moderate head pose variations. To address this pose sensitivity issue and other moderate imaging variations, we propose an analytic Gabor feedforward network which can absorb such moderate changes. Essentially, the network works directly on the raw face images and produces directionally projected Gabor magnitude features at the hidden layer. Subsequently, several sets of magnitude features obtained from various orientations and scales are fused at the output layer for final classification decision. The network model is analytically trained using a single sample per identity. The obtained solution is globally optimal with respect to the classification total error rate. Our empirical experiments conducted on five face data sets (six subsets) from the public domain show encouraging results in terms of identification accuracy and computational efficiency.

People today rely more and more on global positioning system (GPS) for navigation when driving in unfamiliar environments. While GPS navigation is indispensable in an intelligent vehicle and provides convenience for road direction, concerns are also raised if the use of GPS may distract drivers to increase unnecessary stress. In this paper, we explore the effects of using GPS navigation on driver stress utilizing electrocardiogram (ECG) signals. In particular, the effects of higher or lower density of GPS instructions are studied. To analyze the driver stress, eight heart rate variability (HRV) features, which were commonly utilized in human stress related studies, were computed from ECG signals. Statistical significance tests were then performed to each HRV feature, so that those effective features for detecting driver stress may be localized. Our studies, based on road driving experiments with ten healthy subjects, showed that MeanRR, SDNN and HRVTri are the top three effective features to detect driver stress, while frequency domain features in general are not sensitive to driver stress. Based on the effective features, our analysis further showed that basically, driving with higher density of GPS instructions has no significant driver stress difference from driving with lower density of GPS instructions.

In this paper, a single hidden-layer feedforward fusion network is proposed for face identity verification. Essentially, the feature extraction, matching score calculation and fusion algorithm design steps are integrated and absorbed into a hidden layer of the model. Each hidden node works on the raw face image directly and produces an Euclidean distance based match score within the network. These scores are then incorporated with output weights to produce a fused score at the final stage. Our experimental study conducted using three face databases shows that the proposed model consistently outperforms competing methods.

In this paper, we propose a user authentication system based on hand-gesture signature without the need of any handheld device. The system uses a depth image sensor to locate the fingertip and palm mass-center from detected hand region for trajectory processing. Apart from the positional information, the velocity and acceleration information are included as input features for trajectory matching. The system verification performance is evaluated in terms of equal error rate. In addition, an investigation of fusion at feature level is conducted for possible performance enhancement. Our empirical results show the potential of the proposed bare-hand in-air signature system.

This work proposes a structured random projection via feature weighting for cancelable identity verification. Essentially, projected facial features are weighted based on their discrimination capability prior to a matching process. In order to conceal the face identity, an averaging over several templates with different transformations is performed. Finally, several cancelable templates extracted from partial face images are fused at score level via a total error rate minimization. Our empirical experiments on two experimental scenarios using AR, FERET' and Sheffield databases show that the proposed method consistently outperforms competing state-of-the-art unsupervised methods in terms of verification accuracy.