Single sample per person face recognition (SSPP FR), i.e., identifying a person (i.e., data subject) with a single face image only for training, has several attractive potential applications, but it is still a challenging problem. Existing generic learning methods usually leverage prototype plus variation (P+V) model for SSPP FR provided that face samples in the biometric enrolment database are variation-free and thus can be treated as the prototypes of data subjects. However, this condition is not satisfied when these samples are contaminated by nuisance facial variations in the wild, such as varied expressions, poor lightings, and disguises (e.g., wearing scarf). We call this new and practical problem SSPP FR with a c{c} ontaminated biometric e{e} nrolment database (SSPP-ce FR). Subsequently, a challenging issue will be raised on estimating proper prototypes from the contaminated enrolment samples in SSPP-ce FR. Moreover, the generated variation dictionary also needs to be enhanced because it is simply based on the subtraction of average face from the samples of the same data subject in the generic set, thus containing individual characteristics that can hardly be shared by other data subjects. To address these two issues, we propose a novel synergistic generic learning (SGL) method to study the SSPP-ce FR problem. Compared with the existing generic learning methods, SGL develops a new “learned P + learned V” model to identify new query samples. Specifically, it learns better prototypes for the contaminated samples in the biometric enrolment database by preserving their more discriminative subject-specific portions and learns a representative variation dictionary by extracting the less discriminative intra-subject variants from an auxiliary generic set. The experiments on various benchmark face datasets demonstrate the effectiveness of the proposed SGL method.

中文翻译：

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从每个人受污染的单个样本中进行人脸识别的协同通用学习


单样本人脸识别（SSPP FR），即仅用于训练的单个人脸图像识别人（即数据主体），具有一些有吸引力的潜在应用，但它仍然是一个具有挑战性的问题。现有的通用学习方法通​​常利用原型加变异（P+V）模型来进行 SSPP FR，前提是生物特征登记数据库中的人脸样本是无变异的，因此可以被视为数据主体的原型。然而，当这些样本受到野外令人讨厌的面部变化的污染时，例如不同的表情、不良的照明和伪装（例如，戴围巾），则不满足此条件。我们将这个新的实际问题称为带有污染的生物识别数据库的 SSPP FR (SSPP-ce FR)。随后，将提出一个具有挑战性的问题，即从 SSPP-ce FR 中受污染的注册样本中估计适当的原型。此外，生成的变异字典还需要增强，因为它只是简单地基于从通用集中的同一数据主体的样本中减去平均面部，从而包含了其他数据主体难以共享的个体特征。为了解决这两个问题，我们提出了一种新颖的协同通用学习（SGL）方法来研究 SSPP-ce FR 问题。与现有的通用学习方法相比，SGL开发了一种新的“learned P+learned V”模型来识别新的查询样本。具体来说，它通过保留更具辨别力的特定于受试者的部分来学习生物识别登记数据库中受污染样本的更好原型，并通过从辅助通用集中提取辨别力较小的受试者内变体来学习代表性变体字典。 在各种基准人脸数据集上的实验证明了所提出的 SGL 方法的有效性。