Department of Computer Science, University of Calgary, 2500 University Dr. N.W., Calgary, AB T2N1N4, Canada.
Sensors (Basel). 2020 Feb 19;20(4):1133. doi: 10.3390/s20041133.
In recent years, human-machine interactions encompass many avenues of life, ranging from personal communications to professional activities. This trend has allowed for person identification based on behavior rather than physical traits to emerge as a growing research domain, which spans areas such as online education, e-commerce, e-communication, and biometric security. The expression of opinions is an example of online behavior that is commonly shared through the liking of online images. Visual aesthetic is a behavioral biometric that involves using a person's sense of fondness for images. The identification of individuals using their visual aesthetic values as discriminatory features is an emerging domain of research. This paper introduces a novel method for aesthetic feature dimensionality reduction using gene expression programming. The proposed system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40,000 images demonstrate a 95% accuracy of identity recognition based solely on users' aesthetic preferences.
近年来，人机交互涵盖了许多生活领域，从个人通信到专业活动。这种趋势使得基于行为而非身体特征的人员识别成为一个日益增长的研究领域，涵盖了在线教育、电子商务、电子通信和生物识别安全等领域。表达意见是一种常见的在线行为，通常通过喜欢在线图像来分享。视觉审美是一种行为生物识别，涉及到一个人对图像的喜好程度。使用个人的视觉审美价值作为区分特征来识别个人是一个新兴的研究领域。本文提出了一种使用基因表达编程进行审美特征降维的新方法。所提出的系统能够使用基于树的遗传方法进行特征重组。降低特征维度可以提高分类器的准确性、减少计算运行时间并最小化所需的存储空间。在一个由 200 个 Flickr 用户评估 40000 张图像的数据集上获得的结果表明，仅基于用户的审美偏好，身份识别的准确率达到 95%。
