An open research problem in automatic signature verification is the skilled forgery attacks. However, the skilled forgeries are very difficult to acquire for representation learning. To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures. First, a neuromotor inspired signature synthesis method is proposed to synthesize signatures with different distortion levels for any template signature. Then, given the templates, we construct a lightweight one-dimensional convolutional network to learn to rank the synthesized samples, and directly optimize the average precision of the ranking to exploit relative and fine-grained signature similarities. Finally, after training, fixed-length representations can be extracted from dynamic signatures of variable lengths for verification. One highlight of our method is that it requires neither skilled nor random forgeries for training, yet it surpasses the state-of-the-art by a large margin on two public benchmarks.

This paper proposes a psychologically inspired convolutional neural network (PI-CNN) to achieve automatic facial beauty prediction. Different from the previous methods, the PI-CNN is a hierarchical model that facilitates both the facial beauty representation learning and predictor training. Inspired by the recent psychological studies, significant appearance features of facial detail, lighting and color were used to optimize the PI-CNN facial beauty predictor using a new cascaded fine-tuning method. Experiments indicate that the cascaded fine-tuned PI-CNN predictor is robust to facial appearance variances, and obtains the highest correlation of 0.87 in the SCUT-FBP benchmark database, which is superior to the related hand-designed feature and related deep learning methods.

In this paper, we present an effective method to analyze the recognition confidence of handwritten Chinese character, based on the softmax regression score of a high performance convolutional neural network (CNN). Through careful and thorough statistics of 827,685 testing samples that randomly selected from total 8836 different classes of Chinese characters, we find that the confidence measurement based on CNN is an useful metric to know how reliable the recognition results are. Furthermore, we find by experiments that the recognition confidence can be used to find out similar and confusable character-pairs, to check wrongly or cursively written samples, and even to discover and correct mislabeled samples. Many interesting observation and statistics are given and analyzed in this study.

In this paper, we propose a novel contour-based method to beautify online handwritten Chinese character to the Kai style calligraphy. According to the feature and structure of Kai style calligraphy, Bezier curve is used to sketch the user-input stroke segment contour and the corner contour. We get the whole contour path of beautified character by connecting stroke segments' contour and corners' contour end-to-end. Anti-aliasing technology is used to make the edge of the contour fine and smooth. Finally, the path-fill algorithm is adopted to fill the inner of the contour. Our system is proved to be effective and efficient. Meanwhile, users can choose any color for the contour and the inner, which is more convenient for users' design.

Beauty is a universal concept which has long been explored by philosophers, artists and psychologists, but there are few implementations of automated facial beauty assessment in computational science. In this paper, we develop an automated Chinese female facial beauty classification system through the application of machine learning algorithm of SVM (Support Vector Machine). We present a simple but effective feature extraction for facial beauty classification. 17 geometric features are designed to abstractly represent each facial image. The experiment is based on 510 facial images, high accuracy of 95.3% is obtained for 2-level classification (beautiful or not), but the accuracy of 4-level classification is 77.9% by SVM. The results clearly show that the notion of beauty perceived by human can also be learned by machine through the employment of supervised learning techniques. Furthermore, the finding of big gap between the accuracy of 2-level classification and 4-level classification is interesting and surprising: the high accuracy naturally leads to the conclusion that there indeed exists simplicity and objectiveness underlying the judgment of aesthetical ideal facial attractiveness; In contrast, the relatively low accuracy for 4-level classification indicates that the presented simple feature vectors is not sufficient for the classification of other levels of facial attractiveness.