Abstract
Face attribute research has so far used only simple binary attributes for facial hair; e.g., beard / no beard. We have created a new, more descriptive facial hair annotation scheme and applied it to create a new facial hair attribute dataset, FH37K. Face attribute research also so far has not dealt with logical consistency and completeness. For example, in prior research, an image might be classified as both having no beard and also having a goatee (a type of beard). We show that the test accuracy of previous classification methods on facial hair attribute classification drops significantly if logical consistency of classifications is enforced. We propose a logically consistent prediction loss, LCPLoss, to aid learning of logical consistency across attributes, and also a label compensation training strategy to eliminate the problem of no positive prediction across a set of related attributes. Using an attribute classifier trained on FH37K, we investigate how facial hair affects face recognition accuracy, including variation across demographics. Results show that similarity and difference in facial hairstyle have important effects on the impostor and genuine score distributions in face recognition.
Abstract (translated)
面部特征研究目前仅使用简单的二进制特征对面部毛发进行分类,例如胡须/无胡须。我们创造了一个新的更详细的面部毛发标注方案,并将其应用于创建一个新的面部毛发属性数据集FH37K。面部特征研究还尚未处理逻辑一致性和完整性。例如,在先前的研究中,一个图像可能被分类为既有无胡须又有 goatee(一种胡须类型)。我们表明,如果逻辑一致性的分类结果得以强制,先前的面部毛发属性分类方法的性能将大幅下降。我们提出了逻辑一致性预测损失LCPLoss,以帮助学习跨属性的逻辑一致性,并提出了标签补偿训练策略,以消除一组相关属性中不存在积极预测的问题。使用训练在FH37K上的 attribute classifier,我们研究面部毛发对面部识别准确性的影响,包括年龄组的变化。结果表明,面部毛发的发型相似性和差异对人脸识别中的冒牌者和真实得分分布具有重要的影响。
URL
https://arxiv.org/abs/2302.11102