基于特征级和决策级融合的人脸吸引力评价方法

Facial attractiveness evaluation method based on fusion of feature-level and decision-level

在个性化的人脸吸引力的研究中,由于特征缺失和对于大众审美的影响因素考虑不足,导致预测个人偏好无法到达很高的预测精度。为了提高预测精度,提出了一个基于特征级和决策级信息融合的个性化人脸吸引力预测框架。首先,将代表不同人脸美丽特征的客观特性融合到一起,利用特征选择算法挑选出具有代表性的人脸吸引力特征,并利用不同的信息融合策略将人脸局部、全局特征融合起来;然后,将传统的人脸特征与通过深度网络自动提取的特征融合起来。同时,提出多种融合策略进行对比,将代表着大众审美偏好的评分信息与代表个人偏好的个性化评分信息进行决策级融合,最终实现个性化的人脸吸引力预测评分。实验结果表明,相比现有针对个性化人脸吸引力评价研究的算法,所提的多层次融合方法在预测精度方面有显著的提升,能够达到Pearson相关系数0. 9以上。该方法可用于个性化推荐、人脸美化等领域。

In the study of personalized facial attractiveness,due to lack of features and insufficient consideration of the influence factors of public aesthetics,the prediction of personal preferences cannot reach high prediction accuracy.In order to improve the prediction accuracy,a new personalized facial attractiveness prediction framework based on feature-level and decision-level information fusion was proposed.Firstly,the objective characteristics of different facial beauty features were fused together,and the representative facial attractive features were selected by a feature selection algorithm,the local and global features of face were fused by different information fusion strategies.Then,the traditional facial features were fused with the features extracted automatically through deep networks.At the same time,a variety of fusion strategies were proposed for comparison.The score information representing the public aesthetic preferences and the personalized score information representing the individual preferences were fused at the decision level.Finally,the personalized facial attractiveness prediction score was obtained.The experimental results show that,compared with the existing algorithms for personalized facial attractiveness evaluation,the proposed multi-level fusion method has a significant improvement in prediction accuracy,and can achieve the Pearson correlation coefficient more than0.9.The proposed method can be used in the fields of personalized recommendation and face beautification.