Novel Multi-Feature Fusion Facial Aesthetic Analysis Framework

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Huanyu Chen;Weisheng Li;Xinbo Gao;Bin Xiao
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引用次数: 0

Abstract

Machine learning has been used in facial beauty prediction studies. However, the integrity of facial geometric information is not considered in facial aesthetic feature extraction, and the impact of other facial attributes (expression) on aesthetics. We propose a novel multi-feature fusion facial aesthetic analysis framework (NMFA) to overcome this problem. First, we designed a facial shape feature, which is an intuitive, visual quantitative description, based on B-spline. Second, we designed a representative low-dimensional facial structural feature to establish the theoretical basis of the facial structure, based on facial aesthetic structure and expression recognition theory. Next, we designed texture and holistic features based on Gabor and VGG-face network. Finally, we used a multi-feature fusion strategy to fuse them for aesthetic evaluation. Experiments were conducted on four databases. The results revealed that the proposed method realizes the visualization of facial shape features, enriches geometric information, solves the problem of lack of facial geometric information and difficulty to understand, and achieves excellent performance with fewer parameters.
一种新颖的多特征融合人脸美学分析框架
机器学习已被用于面部美容预测研究。然而,在面部美学特征提取中没有考虑面部几何信息的完整性,以及其他面部属性(表情)对美学的影响。为了克服这一问题,我们提出了一种新的多特征融合人脸美学分析框架(NMFA)。首先,我们设计了一个基于B样条的人脸形状特征,这是一个直观、可视化的定量描述。其次,基于人脸美学结构和表情识别理论,设计了具有代表性的低维人脸结构特征,为人脸结构的研究奠定了理论基础。接下来,我们设计了基于Gabor和VGG人脸网络的纹理和整体特征。最后,我们使用了多特征融合策略来融合它们进行美学评价。实验在四个数据库上进行。结果表明,该方法实现了人脸形状特征的可视化,丰富了几何信息,解决了人脸几何信息缺乏和难以理解的问题,并以较少的参数获得了优异的性能。
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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