In this paper, we propose a novel framework leveraging the advantages of the representational ability of deep learning and domain generalization for face spoofing detection. In particular, the generalized deep feature representation is achieved by taking both spatial and temporal information into consideration, and a 3D convolutional neural network architecture tailored for the spatial-temporal input is proposed. The network is first initialized by training with augmented facial samples based on cross-entropy loss and further enhanced with a specifically designed generalization loss, which coherently serves as the regularization term. The training samples from different domains can seamlessly work together for learning the generalized feature representation by manipulating their feature distribution distances. We evaluate the proposed framework with different experimental setups using various databases. Experimental results indicate that our method can learn more discriminative and generalized information compared with the state-of-the-art methods.

Learning Generalized Deep Feature Representation for Face Anti-Spoofing

Robust authentication and identification methods become an indispensable urgent task to protect the integrity of the devices and the sensitive data Passwords have provided access control and authentication, but have shown their inherent vulnerabilities The speed and convenience factor are what makes biometrics the ideal authentication solution as they could have a low probability of circumvention To overcome the limitations of the traditional biometric systems, electrocardiogram (ECG) has received the most attention from the biometrics community due to the highly individualized nature of the ECG signals and the fact that they are ubiquitous and difficult to counterfeit However, one of the main challenges in ECG-based biometric development is the lack of large ECG databases In this paper, we contribute to creating a new large gallery off-the-person ECG datasets that can provide new opportunities for the ECG biometric research community We explore the impact of filtering type, segmentation, feature extraction, and health status on ECG biometric by using the evaluation metrics Our results have shown that our ECG biometric authentication outperforms existing methods lacking the ability to efficiently extract features, filtering, segmentation, and matching This is evident by obtaining 100% accuracy for PTB, MIT-BHI, CEBSDB, CYBHI, ECG-ID, and in-house ECG-BG database in spite of noisy, unhealthy ECG signals while performing five-fold cross-validation In addition, an average of 211% EER among 1,694 subjects is obtained

/pdf/ecg-biometric-authentication-a-comparative-analysis-3q3dbu0y3q.pdf

ECG Biometric Authentication: A Comparative Analysis

There has been an increasing consensus in learning based face anti-spoofing that the divergence in terms of camera models is causing a large domain gap in real application scenarios. We describe a framework that eliminates the influence of inherent variance from acquisition cameras at the feature level, leading to the generalized face spoofing detection model that could be highly adaptive to different acquisition devices. In particular, the framework is composed of two branches. The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain. Motivated by the fact that the spoofing features exist not only in the high frequency domain, in the second branch the discrimination capability of extracted spoofing features is further boosted from the enhanced image based on the recomposition of the high-frequency and low-frequency information. Finally, the classification results of the two branches are fused together by a weighting strategy. Experiments show that the proposed method can achieve better performance in both intra-dataset and cross-dataset settings, demonstrating the high generalization capability in various application scenarios.

Camera Invariant Feature Learning for Generalized Face Anti-Spoofing

Face presentation attack detection based on chromatic co-occurrence of local binary pattern and ensemble learning

With the advancements in technology and growing popularity of facial photo editing in the social media landscape, tools such as face swapping and face morphing have become increasingly accessible to the general public. It opens up the possibilities for different kinds of face presentation attacks, which can be taken advantage of by impostors to gain unauthorized access of a biometric system. Moreover, the wide availability of 3D printers has caused a shift from print attacks to 3D mask attacks. With increasing types of attacks, it is necessary to come up with a generic and ubiquitous algorithm with a panoptic view of these attacks, and can detect a spoofed image irrespective of the method used. The key contribution of this paper is designing a deep learning based panoptic algorithm for detection of both digital and physical presentation attacks using Cross Asymmetric Loss Function (CALF). The performance is evaluated for digital and physical attacks in three scenarios: ubiquitous environment, individual databases, and cross-attack/cross-database. Experimental results showcase the superior performance of the proposed presentation attack detection algorithm.

Crafting A Panoptic Face Presentation Attack Detector

Face recognition technique has been widely applied to personal identification systems due to its satisfying performance. However, its security may be a crucial issue, since many studies have shown that face recognition systems may be vulnerable in an adversarial environment, in which an adversary can camouflage as a legitimate user in order to mislead the system. Although face liveness detection methods have been proposed to distinguish real and fake faces, they are either time-consuming, costly, or sensitive to noise and illumination. This paper proposes a face liveness detection method with flash against 2D spoofing attack. Flash not only can enhance the differentiation between legitimate and illegitimate users, but it also reduces the influence of environmental factors. Two images are taken from a subject, one with flash and another without flash. Four texture and 2D structure descriptors with low computational complexity are used to capture information of the two images in our model. Advantages of our method include low installation cost of flash and no user cooperation required. A data set of 50 subjects collected under different scenarios is used in the experiments to evaluate the proposed method. The experimental results indicate that the proposed model performs better than existing liveness detection methods in different environmental scenarios. This paper confirms that the use of flash successfully improves face liveness detection in terms of accuracy, robustness, and running time.

Face Liveness Detection Using a Flash Against 2D Spoofing Attack

The blood pressure (BP) is generally measured using a cuff based sphygmomanometer. However, it is inconvenient to be used. Recently, an alternative solution only using the photoplethesmograms (PPGs) was proposed. In this case, the continuous BP estimation could be performed. First, the features were extracted from the PPGs. Then, a regression network was employed to estimate the BP values. Nevertheless, the accuracy of this approach was not so high. In order to improve the estimation accuracy, this paper proposes to cascade a two layer piecewise neural network to the output of the existing regression network to correct the estimation error. In particular, the overall system is a three layer network. The first layer of the network is the existing regression network. It generates the initial estimated BP values. The second layer of the network consists of the window functions. It segments the range of the BP values into various regions for the further processing. The final layer of the network performs the estimation correction. The performance of our proposed network is evaluated via two practical datasets and three common regression networks including the three layer artificial neural network (ANN) based regression network, the random forest (RF) based regression network and the support vector regression (SVR) based network. For the first dataset, our proposed method with the RF model and the piecewise neural network achieves the systolic BP (SBP) estimation error and the diastolic BP (DBP) estimation error at <inline-formula> <tex-math notation="LaTeX">$3.01{\pm }2.22$ </tex-math></inline-formula>mmHg with the correlation coefficient at 0.926 and <inline-formula> <tex-math notation="LaTeX">$4.43{\pm }3.37$ </tex-math></inline-formula>mmHg with the correlation coefficient at 0.935, respectively. On the other hand, the conventional RF model without the piecewise neural network achieves the SBP estimation error and the DBP estimation error at <inline-formula> <tex-math notation="LaTeX">$5.34{\pm }4.08$ </tex-math></inline-formula>mmHg with the correlation coefficient at 0.740 and <inline-formula> <tex-math notation="LaTeX">$5.89{\pm }4.98$ </tex-math></inline-formula>mmHg with the correlation coefficient at 0.840, respectively. For the second dataset, our proposed method with the RF model and the piecewise neural network achieves the SBP estimation error and the DBP estimation error at <inline-formula> <tex-math notation="LaTeX">$7.91{\pm }8.06$ </tex-math></inline-formula>mmHg with the correlation coefficient at 0.876 and <inline-formula> <tex-math notation="LaTeX">$3.47{\pm }5.59$ </tex-math></inline-formula>mmHg with the correlation coefficient at 0.859, respectively. On the other hand, the conventional RF model without the piecewise neural network achieves the SBP estimation error and the DBP estimation error at <inline-formula> <tex-math notation="LaTeX">$9.77{\pm }9.01$ </tex-math></inline-formula>mmHg with the correlation coefficient at 0.805 and <inline-formula> <tex-math notation="LaTeX">$7.08{\pm }5.55$ </tex-math></inline-formula>mmHg with the correlation coefficient at 0.612, respectively. It can be seen that our proposed network yields the estimated BP values highly correlated to the reference BP values. Also, our proposed method yields the higher accuracies compared to the existing networks. This demonstrates the effectiveness of our proposed network.

Joint Regression Network and Window Function-Based Piecewise Neural Network for Cuffless Continuous Blood Pressure Estimation Only Using Single Photoplethesmogram

FE-YOLOv5: Feature enhancement network based on YOLOv5 for small object detection

Abstract The traditional blood glucose estimation method requires to take the invasive measurements several times a day. Therefore, it has a high infection risk and the users are suffered from the pain. Moreover, the long term consumable cost is high. Recently, the wearable and non‐invasive blood glucose estimation approach has been proposed. However, due to the unreliability of the acquisition device, the presence of the noise and the variations of the acquisition environments, the obtained features and the reference blood glucose values are highly unreliable. Moreover, different subjects have different responses of the infrared light to the blood glucose. To address this issue, a polynomial fitting approach to smooth the obtained features or the reference blood glucose values has been proposed. In particular, the design of the coefficients in the polynomial is formulated as the various optimisation problems. First, the blood glucose values are estimated based on the individual optimisation approaches. Second, the absolute difference values between the estimated blood glucose values and the actual blood glucose values based on each optimisation approach are computed. Third, these absolute difference values for each optimisation approach are sorted in the ascending order. Fourth, for each sorted blood glucose value, the optimisation method corresponding to the minimum absolute difference value is selected. Fifth, the accumulate probability of each selected optimisation method is computed. If the accumulate probability of any selected optimisation method at a point is greater than a threshold value, then the accumulate probabilities of these three selected optimisation methods at that point are reset to zero. A range of the sorted blood glucose values are defined as that with the corresponding boundaries points being the previous reset point and this reset point. Hence, after performing the above procedures for all the sorted reference blood glucose values in the validation set, the regions of the sorted reference blood glucose values and the corresponding optimisation methods in these regions are determined. It is worth noting that the conventional lowpass denoising method was performed in the signal domain (either in the time domain or in the frequency domain), while the authors’ proposed method is performed in the feature space or the reference blood glucose space. Hence, the authors’ proposed method can further improve the reliability of the obtained feature values or the reference blood glucose values so as to improve the accuracy of the blood glucose estimation. Moreover, the individual modelling regression method has been employed here to suppress the effects of different users having different responses of the infrared light to the blood glucose values. The computer numerical simulation results show that the authors’ proposed method yields the mean absolute relative deviation (MARD) at 0.0930 and the percentage of the test data falling in the zone A of the Clarke error grid at 94.1176%.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/syb2.12063

Danni Chen

Papers

Face Liveness Detection Using a Flash Against 2D Spoofing Attack

Joint Regression Network and Window Function-Based Piecewise Neural Network for Cuffless Continuous Blood Pressure Estimation Only Using Single Photoplethesmogram

FE-YOLOv5: Feature enhancement network based on YOLOv5 for small object detection

Fusion of various optimisation based feature smoothing methods for wearable and non‐invasive blood glucose estimation