Neural Aggregation Network for Video Face Recognition
Jiaolong Yang,Peiran Ren,Dongqing Zhang,Dong Chen,Fang Wen,Hongdong Li,Gang Hua +6 more
- pp 5216-5225
TLDR
This NAN is trained with a standard classification or verification loss without any extra supervision signal, and it is found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces.Abstract:
This paper presents a Neural Aggregation Network (NAN) for video face recognition. The network takes a face video or face image set of a person with a variable number of face images as its input, and produces a compact, fixed-dimension feature representation for recognition. The whole network is composed of two modules. The feature embedding module is a deep Convolutional Neural Network (CNN) which maps each face image to a feature vector. The aggregation module consists of two attention blocks which adaptively aggregate the feature vectors to form a single feature inside the convex hull spanned by them. Due to the attention mechanism, the aggregation is invariant to the image order. Our NAN is trained with a standard classification or verification loss without any extra supervision signal, and we found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces. The experiments on IJB-A, YouTube Face, Celebrity-1000 video face recognition benchmarks show that it consistently outperforms naive aggregation methods and achieves the state-of-the-art accuracy.read more
Citations
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Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
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Deep Face Recognition: A Survey
TL;DR: The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues with some open issues currently overlooked by the community.
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Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification
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