Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment
Computational Visual Media Lecture Notes in Computer Science(2021)
摘要
Face recognition has made significant progress in recent years due to deep
convolutional neural networks (CNN). In many face recognition (FR) scenarios,
face images are acquired from a sequence with huge intra-variations. These
intra-variations, which are mainly affected by the low-quality face images,
cause instability of recognition performance. Previous works have focused on
ad-hoc methods to select frames from a video or use face image quality
assessment (FIQA) methods, which consider only a particular or combination of
several distortions.
In this work, we present an efficient non-reference image quality assessment
for FR that directly links image quality assessment (IQA) and FR. More
specifically, we propose a new measurement to evaluate image quality without
any reference. Based on the proposed quality measurement, we propose a deep
Tiny Face Quality network (tinyFQnet) to learn a quality prediction function
from data.
We evaluate the proposed method for different powerful FR models on two
classical video-based (or template-based) benchmark: IJB-B and YTF. Extensive
experiments show that, although the tinyFQnet is much smaller than the others,
the proposed method outperforms state-of-the-art quality assessment methods in
terms of effectiveness and efficiency.
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