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Volume: 31 | Article ID: art00003
Face Set Recognition
  DOI :  10.2352/ISSN.2470-1173.2019.8.IMAWM-400  Published OnlineJanuary 2019

In this paper we present a Cluster Aggregation Network (CAN) for face set recognition. This network takes a set of face images, which could be either face videos or clusters with a different number of face images as its input, and then it is able to produce a compact and fixed-dimensional feature representation for the face set for the purpose of recognition. The whole network is made up of two modules, among which the first one is a face feature embedding module and the second one is the face feature aggregation module. The first module is a deep Convolutional Neural Network (CNN) which maps each of the face images to a fixed-dimensional vector. The second module is also a CNN which is trained to be able to automatically assess the quality of input face images and thus assign various weights to the images’ corresponding feature vectors. Then the one aggregated feature vector representing the input set is formed inside the convex hull formed by the input single face image features. Due to the mechanism that quality assessment is invariant to the order of one image in a set and the number of images in the set, the aggregation is invariant to these factors. Our CAN is trained with standard classification loss without any other supervision information and we found that our network is automatically attracted to high quality face images, while repelling low quality images, such as blurred, blocked, and non-frontal face images. We trained our networks with CASIA and YouTube Face datasets and the experiments on IJB-C video face recognition benchmark show that our method outperforms the current state-of-the-art feature aggregation methods and our challenging baseline aggregation method.

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Tongyang Liu, Xiaoyu Xiang, Qian Lin, Jan P Allebach, "Face Set Recognitionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World,  2019,  pp 400-1 - 400-6,

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