LAMP-HQ: A Large-Scale Multi-Pose High-Quality Database and Benchmark for NIR-VIS Face Recognition

TL;DR

This paper introduces LAMP-HQ, a large-scale NIR-VIS face recognition database, and benchmarks various methods, proposing a novel spectral attention network to improve cross-spectral face recognition performance.

Contribution

The paper presents a new extensive NIR-VIS face database and a spectral attention network, advancing the training resources and techniques for heterogeneous face recognition.

Findings

LAMP-HQ contains 56,788 NIR and 16,828 VIS images of 573 subjects.

The spectral attention network improves recognition accuracy across multiple datasets.

Benchmark results demonstrate the effectiveness of the proposed methods.

Abstract

Near-infrared-visible (NIR-VIS) heterogeneous face recognition matches NIR to corresponding VIS face images. However, due to the sensing gap, NIR images often lose some identity information so that the recognition issue is more difficult than conventional VIS face recognition. Recently, NIR-VIS heterogeneous face recognition has attracted considerable attention in the computer vision community because of its convenience and adaptability in practical applications. Various deep learning-based methods have been proposed and substantially increased the recognition performance, but the lack of NIR-VIS training samples leads to the difficulty of the model training process. In this paper, we propose a new Large-Scale Multi-Pose High-Quality NIR-VIS database LAMP-HQ containing 56,788 NIR and 16,828 VIS images of 573 subjects with large diversities in pose, illumination, attribute, scene and accessory. We furnish a benchmark along with the protocol for NIR-VIS face recognition via generation on LAMP-HQ, including Pixel2Pixel, CycleGAN, and ADFL. Furthermore, we propose a novel exemplar-based variational spectral attention network to produce high-fidelity VIS images from NIR data. A spectral conditional attention module is introduced to reduce the domain gap between NIR and VIS data and then improve the performance of NIR-VIS heterogeneous face recognition on various databases including the LAMP-HQ.