Soft biometrics, although not discriminant enough for person recognition provides additional information that aids traditional person recognition. Initially, attempts were made to integrate appearance-based facial soft biometrics, such as facial marks, skin color, and hair color/style, but more recently behavior-based facial soft biometrics, such as head dynamics, visual speech, and facial expressions have also been studied. Facial expressions are further classified as macro and micro-expressions and most of the existing studies using facial expressions as a soft biometric have focused on macro-expressions. Therefore, in this study, we investigate the utility of micro-expressions as a soft biometric for person recognition. The proposed system is based on the fusion of traditional facial features that model the facial appearance with soft biometric features that model the micro-expressions in an image sequence. We tested a texture-based traditional feature extraction technique, two motion-based soft biometric techniques, and several fusion methods at feature, rank, and decision level. The experiments were conducted on three commonly used micro-expression databases and exhibit an improvement of around 5% identification rate when soft biometric traits are fused with traditional face recognition at decision level.

软的生物识别技术尽管不能对人的识别进行足够的区分，但它提供了有助于传统人识别的其他信息。最初，尝试整合基于外观的面部软生物特征，例如面部标记，皮肤颜色和头发的颜色/样式，但是最近，基于行为的面部软生物特征，例如头部动态，视觉语音和面部表情，已经集成在一起也进行了研究。面部表情被进一步分类为宏观和微观表达，并且大多数使用面部表情作为软生物特征的现有研究都集中在宏观表达上。因此，在这项研究中，我们调查了微表达作为一种用于人识别的软生物测定法的效用。所提出的系统基于将对面部外观建模的传统面部特征与对图像序列中的微表情建模的软生物特征的融合。我们测试了基于纹理的传统特征提取技术，两种基于运动的软生物特征技术以及几种在特征，等级和决策级别的融合方法。实验是在三个常用的微表达数据库上进行的，当将软生物特征与传统人脸识别在决策层融合时，其识别率提高了5％。