AcFR: Active Face Recognition Using Convolutional Neural Networks

Masaki Nakada, Han Wang, Demetri Terzopoulos
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引用次数: 28

Abstract

We propose AcFR, an active face recognition system that employs a convolutional neural network and acts consistently with human behaviors in common face recognition scenarios. AcFR comprises two main components—a recognition module and a controller module. The recognition module uses a pre-trained VGG-Face net to extract facial image features along with a nearest neighbor identity recognition algorithm. Based on the results, the controller module can make three different decisions—greet a recognized individual, disregard an unknown individual, or acquire a different viewpoint from which to reassess the subject, all of which are natural reactions when people observe passers-by. Evaluated on the PIE dataset, our recognition module yields higher accuracy on images under closer angles to those saved in memory. The accuracy is viewdependent and it also provides evidence for the proper design of the controller module.
基于卷积神经网络的主动人脸识别
我们提出AcFR,一种主动人脸识别系统,它采用卷积神经网络,并在常见的人脸识别场景中与人类行为保持一致。AcFR由两个主要部分组成:识别模块和控制器模块。识别模块使用预训练的VGG-Face网络,结合最近邻身份识别算法提取人脸图像特征。根据结果,控制器模块可以做出三种不同的决定——与认识的人打招呼,无视未知的人,或者从不同的角度重新评估对象,这些都是人们观察过路人时的自然反应。在PIE数据集上进行评估,我们的识别模块在更接近内存中保存的图像的角度下产生更高的准确性。其精度与视图相关,也为控制器模块的合理设计提供了依据。
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