In recent years, visible-spectrum face verification systems have been shown to match the performance of experienced forensic examiners. However, such systems are ineffective in low-light and nighttime conditions. Thermal face imagery, which captures body heat emissions, effectively augments the visible spectrum, capturing discriminative facial features in scenes with limited illumination. Due to the increased cost and difficulty of obtaining diverse, paired thermal and visible spectrum datasets, not many algorithms and large-scale benchmarks for low-light recognition are available. This paper presents an algorithm that achieves state-of-the-art performance on both the ARL-VTF and TUFTS multi-spectral face datasets. Importantly, we study the impact of face alignment, pixel-level correspondence, and identity classification with label smoothing for multi-spectral face synthesis and verification. We show that our proposed method is widely applicable, robust, and highly effective. In addition, we show that the proposed method significantly outperforms face frontalization methods on profile-to-frontal verification. Finally, we present MILAB-VTF(B), a challenging multi-spectral face dataset that is composed of paired thermal and visible videos. To the best of our knowledge, with face data from 400 subjects, this dataset represents the most extensive collection of indoor and long-range outdoor thermal-visible face imagery. Lastly, we show that our end-to-end thermal-to-visible face verification system provides strong performance on the MILAB-VTF(B) dataset.
翻译:近些年来,显性光谱面对面的核查系统被证明与有经验的法医检查人员的表现相匹配,然而,在低光度和夜间条件下,这种系统是无效的。热面图像记录了体热排放,有效地扩大了可见频谱,在光度有限的场景中捕捉了歧视性的面部特征。由于获取多样化、配对热和可见频谱数据集的成本增加和难度增加,没有提供许多低光度识别的算法和大规模基准。本文展示了一种在ARL-VTF和TUFTS多光谱面像数据集两方面都达到最新业绩的算法。重要的是,我们研究了面部校准、像素级通信和身份分类的影响,并标注了多光谱面面面的合成和核查。我们拟议的方法广泛适用、稳健且非常有效。此外,我们还表明,拟议的方法在直线对面的核查中面临前方面面图像化方法。最后,我们向MIAB-VTF(B)展示了一个具有挑战性的多光谱图像系统,从我们最清晰的面面面面面面面图像数据采集的数据集,从我们向最后显示数据。