Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis

(Aim)COVID-19 is an ongoing infectious disease.It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021.Traditional computer vision methods have achieved promising results on the automatic smart diagnosis.(Method)This study aims to propose a novel deep learning method that can obtain better performance.We use the pseudo-Zernike moment(PZM),derived from Zernike moment,as the extracted features.Two settings are introducing:(i)image plane over unit circle;and(ii)image plane inside the unit circle.Afterward,we use a deep-stacked sparse autoencoder(DSSAE)as the classifier.Besides,multiple-way data augmentation is chosen to overcome overfitting.The multiple-way data augmentation is based on Gaussian noise,salt-and-pepper noise,speckle noise,horizontal and vertical shear,rotation,Gamma correction,random translation and scaling.(Results)10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06%±1.54%,a specificity of 92.56%±1.06%,a precision of 92.53%±1.03%,and an accuracy of 92.31%±1.08%.Its F1 score,MCC,and FMI arrive at 92.29%±1.10%,84.64%±2.15%,and 92.29%±1.10%,respectively.The AUC of our model is 0.9576.(Conclusion)We demonstrate“image plane over unit circle”can get better results than“image plane inside a unit circle.”Besides,this proposed PZM-DSSAE model is better than eight state-of-the-art approaches.

Two-Factor Cancelable Biometrics Authenticator

Biometrics-based authentication system offers advantages of providing high reliability and accuracy. However the contemporary authentication system is impuissance to compromise. If a biometrics data is compromised, it cannot be replaced and rendered unusable. In this paper, a cancelable biometrics-based authenticator is proposed to solve this irrevocability issue. The proposed approach is a two-factor authentication system, which requires both of the random data and facial feature in order to access the system. In this system, tokenized pseudo-random data is coupled with momentbased facial feature via inner product algorithm. The output of the product is then discretized to generate a set of private binary code, coined as 2factor-Hashing code, which is acted as verification key. If this biometrics-based verification key is compromised, a new one can be issued by replacing a different set of random number via token replacement. Then, the compromised one is rendered completely useless. This feature offers an extra protection layer against biometrics fabrication since the verification code is replaceable. Experimental results demonstrate that the proposed system provides zero Equal Error Rate in which there is a clear separation in between the genuine and the imposter distribution populations.