论文标题
自适应面部识别系统是否仍然需要？ APE数据集的实验
Are Adaptive Face Recognition Systems still Necessary? Experiments on the APE Dataset
论文作者
论文摘要
在过去的五年中，深度学习方法，尤其是CNN，在基于面部的识别领域引起了很大的关注，从而取得了令人印象深刻的结果。尽管取得了这种进步，但尚不清楚深度特征能够遵循脸部随着时间而显示的所有类内的变化。在本文中，我们通过采用面部模板的自我更新策略来调查面部识别系统的性能提高。为此，我们评估了众所周知的深度学习面表表示的性能，即面部，在数据集中，我们生成了在较大的捕获时间范围内显式地构想用户在嵌入了阶级的内部变化的数据集中：Aphotoeveryday（APE）数据集。此外，我们将这些深度功能与使用BSIF算法提取的手工制作的功能进行了比较。在这两种情况下，我们都评估了各种模板更新策略，以便检测到此类功能最有用的策略。实验结果表明，“优化”自更换方法在没有更新或随机选择模板的情况下的有效性。
In the last five years, deep learning methods, in particular CNN, have attracted considerable attention in the field of face-based recognition, achieving impressive results. Despite this progress, it is not yet clear precisely to what extent deep features are able to follow all the intra-class variations that the face can present over time. In this paper we investigate the performance the performance improvement of face recognition systems by adopting self updating strategies of the face templates. For that purpose, we evaluate the performance of a well-known deep-learning face representation, namely, FaceNet, on a dataset that we generated explicitly conceived to embed intra-class variations of users on a large time span of captures: the APhotoEveryday (APE) dataset. Moreover, we compare these deep features with handcrafted features extracted using the BSIF algorithm. In both cases, we evaluate various template update strategies, in order to detect the most useful for such kind of features. Experimental results show the effectiveness of "optimized" self-update methods with respect to systems without update or random selection of templates.
