Abstract The boosting on the need of security notably increased the amount of possible facial recognition applications, especially due to the success of the Internet of Things (IoT) paradigm. However, although handcrafted and deep learning-inspired facial features reached a significant level of compactness and expressive power, the facial recognition performance still suffers from intra-class variations such as ageing, facial expressions, lighting changes, and pose. These variations cannot be captured in a single acquisition and require multiple acquisitions of long duration, which are expensive and need a high level of collaboration from the users. Among others, self-update algorithms have been proposed in order to mitigate these problems. Self-updating aims to add novel templates to the users’ gallery among the inputs submitted during system operations. Consequently, computational complexity and storage space tend to be among the critical requirements of these algorithms. The present paper deals with the above problems by a novel template-based self-update algorithm, able to keep over time the expressive power of a limited set of templates stored in the system database. The rationale behind the proposed approach is in the working hypothesis that a dominating mode characterises the features’ distribution given the client. Therefore, the key point is to select the best templates around that mode. We propose two methods, which are tested on systems based on handcrafted features and deep-learning-inspired autoencoders at the state-of-the-art. Three benchmark data sets are used. Experimental results confirm that, by effective and compact feature sets which can support our working hypothesis, the proposed classification-selection approaches overcome the problem of manual updating and, in case, stringent computational requirements.

中文翻译：

---

一种用于基于模板的人脸识别系统自更新的新型分类选择方法

摘要 安全需求的增加显着增加了可能的面部识别应用程序的数量，特别是由于物联网 (IoT) 范式的成功。然而，尽管手工制作和受深度学习启发的面部特征达到了显着的紧凑度和表达能力，但面部识别性能仍然受到诸如老化、面部表情、光照变化和姿势等内部变化的影响。这些变化无法在单次采集中捕获，需要多次长时间采集，成本高昂且需要用户的高度协作。其中，已经提出了自更新算法以减轻这些问题。自我更新的目的是在系统操作期间提交的输入中向用户图库添加新模板。因此，计算复杂性和存储空间往往是这些算法的关键要求之一。本论文通过一种新颖的基于模板的自更新算法来解决上述问题，该算法能够随着时间的推移保持系统数据库中存储的有限模板集的表达能力。所提出的方法背后的基本原理在于工作假设，即主导模式表征给定客户端的特征分布。因此，关键是围绕该模式选择最佳模板。我们提出了两种方法，它们在基于最先进的手工特征和受深度学习启发的自动编码器的系统上进行了测试。使用了三个基准数据集。