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Opera as the essence of Chinese traditional culture, represents the charm and style of Chinese culture. And the using of animation & comic product to inherit traditional Chinese opera culture is not only an extension of traditional Chinese opera, but also a kind of innovation and development of traditional opera. But as for the current development of opera animation & comic product, the lack...
Face Super Resolution(FSR) is to infer High Resolution(HR) facial images from given Low Resolution(LR) ones with the assistance of LR and HR training pairs. Among existing methods, local patch based methods are superior in visual and objective quality than global based methods. These local patch based methods are based on the consistency assumption that the neighbors in HR/LR space form similar local...
Structured sparsity, as an extension of standard sparsity, has shown the outstanding performance when dealing with some highly correlated variables in computer vision and pattern recognition. However, the traditional mixed (L1, L2) or (L1, $\text{L}_{\infty }$ ) group norm becomes weak in characterizing the internal structure of each group, since they cannot alleviate the correlations between variables...
Deep learning is well known as a method to extract hierarchical representations of data. In this paper a novel unsupervised deep learning based methodology, named Local Binary Pattern Network (LBPNet), is proposed to efficiently extract and compare high-level over-complete features in multilayer hierarchy. The LBPNet retains the same topology of Convolutional Neural Network (CNN) — one of the most...
This paper proposes an image-to-image face recognition algorithm that uses Dual Linear Regression based Classification (DLRC) and an Electoral College voting approach. Each face image involved is first converted into a cluster of images; each image in the cluster is obtained by shifting the original image a few pixels. The similarity of a pair of face images can be measured by comparing the distance...
Face Super Resolution(FSR) is to infer High Resolution(HR) facial images from given Low Resolution(LR) ones with the assistance of LR and HR training pairs. Among existing methods, local patch based methods are superior in visual and objective quality than global based methods. These local patch based methods are based on the consistency assumption that the neighbors in HR/LR space form similar local...
In a video based face identification task, a sequence of frames can be utilized to identify the subject in the video. The information extracted from frames can provide samples of the subject in different head poses and facial expressions and under various lighting conditions which enriches the training process. However, some of these frames may not be useful for identification due to noise from various...
By incorporating the priors that human face is a class of highly structured object, position-patch based face hallucination methods represent the test image patch through the same position patches of training faces by employing least square estimation or sparse coding. Due to they cannot provide unbiased approximations or ignore the influence of spatial distances between the test image patch and training...
Most state-of-the-art face hallucination approaches suffer from complicated learning patterns and highly intensive computation, which will lead to low efficiency and considerable computing resources. Therefore, how to restore real face image quickly and efficiently is still an important issue in this field. To solve or partially solve the problem, this paper proposed a novel facial standard deviation...
Among many illumination robust approaches, scale-space decomposition based methods play an important role to reduce the lighting effects in face images. However, most of the existing scale-space decomposition methods perform recognition, based on the illumination-invariant small-scale features only. We propose a scale-space decomposition based face recognition approach that extracts the features of...
This paper describes a novel method for single-image super-resolution (SR) based on a neighbor embedding technique which uses coupled feature spaces under surveillance scenarios. For surveillance face images, traditional neighbor embedding SR approaches could not offer counterintuitive results because consistency between high resolution images and low resolution images is destroyed by serious noise...
Face verification is defined as a person whose identity is claimed a priori will be compared with the person's individual template in database, and then the system checks whether the similarity between pattern and template is sufficient to provide access. In this paper we introduce a new procedure of face verification with an embedding Electoral College framework, which has been applied successfully...
Face recognition has become a very important field of AI, with many competing techniques, both holistic and local. Recently, a new framework for embedding holistic face recognition algorithms into a regional voting approach, has been shown to be a very stable and accurate mechanism for face recognition. A new system is proposed, which extends the regional voting concept and adds weights to each region...
For a class of pattern recognition problems, such as the face recognition problem, humans do not know the strategies that our brains employ in daily life and therefore there is no algorithm that can emulate our brain ability. Without understanding the psychological processes of brains, an objective of improving accuracy of such systems leads nowhere but to a trial-and-error process of different algorithms...
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