Deep Tiny Network for Recognition-Oriented Face Image Quality Assessment

TL;DR

This paper introduces tinyFQnet, a compact deep learning model that effectively assesses face image quality for recognition tasks without reference images, improving recognition stability in variable video scenarios.

Contribution

The paper presents a novel, efficient non-reference face image quality assessment method directly linked to face recognition, using a small deep network called tinyFQnet.

Findings

tinyFQnet outperforms state-of-the-art methods in accuracy.

The method is more efficient and effective on benchmark datasets.

It improves recognition stability in low-quality face images.

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.