Automatic analysis of facial attractiveness from video
Automatic analysis of facial attractiveness from video
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DOI：
10.1109/icip.2014.7025851
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发表时间：
2014-10
期刊：
影响因子：
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通讯作者：
Sacide Kalayci;H. K. Ekenel;H. Gunes
中科院分区：
文献类型：
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作者：
Sacide Kalayci;H. K. Ekenel;H. Gunes
在计算机科学领域，人们对面部美貌和吸引力的自动分析和识别越来越感兴趣。大多数已提出的研究试图使用单一的静态面部图像来建模和预测面部吸引力。虽然静态图像提供的关于面部吸引力的信息有限，但使用包含面部运动和动态行为信息的视频剪辑可以为分析面部吸引力提供更丰富的理解和宝贵的见解。基于这一动机，我们提出使用从视频片段中获得的动态特征和从静态帧中获得的静态特征来自动分析面部吸引力。利用支持向量机(SVM)和随机森林(RF)利用提取的特征来创建和训练吸引力模型。实验结果表明，组合静态和动态特征比单独使用其中任何一个特征集都能提高性能，其中支持向量机提供了最好的预测性能。
There has been a growing interest in the computer science field for automatic analysis and recognition of facial beauty and attractiveness. Most of the proposed studies attempt to model and predict facial attractiveness using a single static facial image. While a static image provides limited information about facial attractiveness, using a video clip that contains information about the motion and the dynamic behaviour of the face provides a richer understanding and valuable insights into analysing facial attractiveness. With this motivation, we propose to use dynamic features obtained from video clips along with static features obtained from static frames for automatic analysis of facial attractiveness. Support Vector Machine (SVM) and Random Forest (RF) are utilised to create and train models of attractiveness using the features extracted. Experimental results show that combining static and dynamic features improve performance over using either of these feature sets alone, and SVM provides the best prediction performance.