Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment

• URL: http://arxiv.org/abs/2106.04852v2
• Date: Sun, 21 Jan 2024 10:54:55 GMT
• Title: Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment
• Authors: Baoyun Peng, Min Liu, Zhaoning Zhang, Kai Xu, Dongsheng Li
• Abstract summary: In many face recognition (FR) scenarios, face images are acquired from a sequence with huge intra-variations. We present an efficient non-reference image quality assessment for FR that directly links image quality assessment (IQA) and FR. Based on the proposed quality measurement, we propose a deep Tiny Face Quality network (tinyFQnet) to learn a quality prediction function from data.
• Score: 26.792481400792376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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