Chinese calligraphy, as a well-known performing art form, occupies an important role in the intangible cultural heritage of China. Previous studies focused on the psychophysiological benefits of Chinese calligraphy. Little attention has been paid to its aesthetic attributes and effectiveness on the cognitive process. To complement our understanding of Chinese calligraphy, this study investigated the aesthetic experience of Chinese cursive-style calligraphy using brain functional network analysis. Subjects stayed on the coach and rested for several minutes. Then, they were requested to appreciate artwork of cursive-style calligraphy. Results showed that (1) changes in functional connectivity between fronto-occipital, fronto-parietal, bilateral parietal, and central–occipital areas are prominent for calligraphy condition, (2) brain functional network showed an increased normalized cluster coefficient for calligraphy condition in alpha2 and gamma bands. These results demonstrate that the brain functional network undergoes a dynamic reconfiguration during the aesthetic experience of Chinese calligraphy. Providing evidence that the aesthetic experience of Chinese calligraphy has several similarities with western art while retaining its unique characters as an eastern traditional art form.

To narrow the inherent sensing gap in heterogeneous face recognition (HFR), recent methods have resorted to generative models and explored the ?recognition via generation? framework. Even though, it remains a very challenging task to synthesize photo-realistic visible faces (VIS) from near-infrared (NIR) images especially when paired training data are unavailable. We present an approach to avert the data misalignment problem and faithfully preserve pose, expression and identity information during cross-spectral face hallucination. At the pixel level, we introduce an unsupervised attention mechanism to warping that is jointly learned with the generator to derive pixel-wise correspondence from unaligned data. At the image level, an auxiliary generator is employed to facilitate the learning of mapping from NIR to VIS domain. At the domain level, we first apply the mutual information constraint to explicitly measure the correlation between domains and thus benefit synthesis. Extensive experiments on three heterogeneous face datasets demonstrate that our approach not only outperforms current state-of-the-art HFR methods but also produce visually appealing results at a high resolution.

Orientation Field (OF) is one of the most significant characters to distinguish fingerprint images from non-fingerprint images. An effective definition of fingerprint OF pattern will not only benefit fingerprint enhancement, but also contribute to latent fingerprint detection and segmentation. The existing fingerprint OF models either require pre-knowledge of singular points, or cannot be generalized to all kinds of fingerprint OFs. In this paper, we propose to define the fingerprint OF patterns based on low rank decomposition and sparse coding. Then we apply this proposed method to fingerprint OF recognition and detection. Experimental results prove the effectiveness of our method.

Overlapped fingerprints are commonly encountered in latent fingerprints lifted from crime scenes. Such overlapped fingerprints can hardly be processed by state-of-the-art fingerprint matchers. Several methods have been proposed to separate the overlapped fingerprints. However, these methods neither provide a robust separation results, nor could be generalized to most overlapped fingerprints. In this paper, we propose a novel overlapped fingerprint separation algorithm based on adaptive orientation model fitting. Different from existing methods, our algorithm estimates the initial orientation fields in a more accurate way and then separates the orientation fields for component fingerprints through an iterative correction process. Experimental results on latent overlapped fingerprints database demonstrate the advantage of our algorithm.

Overlapped fingerprints are commonly encountered in latent fingerprints lifted from crime scenes. Such overlapped fingerprints can hardly be processed by state-of-the-art fingerprint matchers. Several methods have been proposed to separate the overlapped fingerprints. However, these methods neither provide robust separation results, nor could be generalized for most overlapped fingerprints. In this paper, we propose a novel latent overlapped fingerprints separation algorithm based on adaptive orientation model fitting. Different from existing methods, our algorithm estimates the initial orientation fields in a more accurate way and then separates the orientation fields for component fingerprints through an iterative correction process. Global orientation field models are used to predict and correct the orientations in overlapped regions. Experimental results on the latent overlapped fingerprints database show that the proposed algorithm outperforms the state-of-the-art algorithm in terms of accuracy.