Largest Matching Areas for Illumination and Occlusion Robust Face Recognition

In this paper, we introduce a novel approach to face recognition which simultaneously tackles three combined challenges: 1) uneven illumination; 2) partial occlusion; and 3) limited training data. The new approach performs lighting normalization, occlusion de-emphasis and finally face recognition, based on finding the largest matching area (LMA) at each point on the face, as opposed to traditional fixed-size local area-based approaches. Robustness is achieved with novel approaches for feature extraction, LMA-based face image comparison and unseen data modeling. On the extended YaleB and AR face databases for face identification, our method using only a single training image per person, outperforms other methods using a single training image, and matches or exceeds methods which require multiple training images. On the labeled faces in the wild face verification database, our method outperforms comparable unsupervised methods. We also show that the new method performs competitively even when the training images are corrupted.

Electron-impact excitation of Li to high principal quantum numbers

The time-dependent close-coupling method is used to calculate electron-impact excitation cross sections for the Li(2s)--{\textgreater}Li(nl) and Li(2p)--{\textgreater}Li(nl) transitions at incident energies just above the ionization threshold. The implementation of the time-dependent close-coupling method on a nonuniform lattice allows the study of continuum-coupling effects in excitations to high principal quantum number, i.e., n{\textless}=10. Good agreement is found with R-matrix with pseudostates calculations, which also include continuum-coupling effects, for excitations to low principal quantum number, i.e., n{\textless}=4. Poor agreement is found with standard distorted-wave calculations for excitations to all principal quantum numbers, with differences still at the 50% level for n=10. We are able to give guidance as to the accuracy expected in the n3 extrapolation of nonperturbative close-coupling calculations of low n cross sections and rate coefficients.

Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

With the rapid development of internet-of-things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper we propose a new ensemble approach – Many-Kernel Random Discriminant Analysis (MK-RDA) to discover discriminative patterns from chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.