In this article, we introduce an algorithms by incorporating inertial terms in the extragradient algorithm. A weak convergence theorem is established for the proposed algorithm. Numerical experiments show that the inertial algorithms speed up the original ones.

Existing studies on knowledge distillation typically focus on teacher-centered methods, in which the teacher network is trained according to its own standards before transferring the learned knowledge to a student one. However, due to differences in network structure between the teacher and the student, the knowledge learned by the former may not be desired by the latter. Inspired by human educational wisdom, this paper proposes a Student-Centered Distillation (SCD) method that enables the teacher network to adjust its knowledge transfer according to the student network's needs. We implemented SCD based on various human educational wisdom, e.g., the teacher network identified and learned the knowledge desired by the student network on the validation set, and then transferred it to the latter through the training set. To address the problems of current deficiency knowledge, hard sample learning and knowledge forgetting faced by a student network in the learning process, we introduce and improve Proportional-Integral-Derivative (PID) algorithms from automation fields to make them effective in identifying the current knowledge required by the student network. Furthermore, we propose a curriculum learning-based fuzzy strategy and apply it to the proposed PID control algorithm, such that the student network in SCD can actively pay attention to the learning of challenging samples after with certain knowledge. The overall performance of SCD is verified in multiple tasks by comparing it with state-of-the-art ones. Experimental results show that our student-centered distillation method outperforms existing teacher-centered ones.

Finger-vein recognition has been considered as one of the most convenient and effective biometric ways for personal identification. Extracting vein characteristics is crucial for finger-vein classification. However, the finger-vein extraction results always are greatly sensitive to noises due to the low contrast of finger-vein images. To robustly exploit real vein information, this paper proposes a novel method of finger-vein enhancement based on multi-channel Gabor filters. Firstly, multi-channel Gabor filters are used to prominently protrude vein vessel information with variances in widths and orientations in images. The vein information in different scales and orientations of Gabor filters is then combined together to generate an enhanced finger-vein image using a reconstruction rule. Experimental results show that the proposed method is capable of enhancing finger-vein images effectively and reliably.

Finger-vein recognition has received increased attention recently. However, the finger-vein images are always captured in poor quality. This certainly makes finger-vein feature representation unreliable, and further impairs the accuracy of finger-vein recognition. In this paper, we first give an analysis of the intrinsic factors causing finger-vein image degradation, and then propose a simple but effective image restoration method based on scattering removal. To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues. Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration. Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy.

Illumination always affects image quality seriously in practice. To weaken illumination effect on image quality, this paper proposes an adaptive gamma correction method. First, a mapping between pixel and gamma values is built. The gamma values are then revised using two non-linear functions to prevent image distortion. Experimental results demonstrate that the proposed method performs better in readjusting image illumination condition and improving image quality.