Hermosilla Gabriel, Gallardo Francisco, Farias Gonzalo, San Martin Cesar
Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, Chile.
Department of Electrical Engineering, University of La Frontera, Temuco 4811230, Chile.
Sensors (Basel). 2015 Jul 23;15(8):17944-62. doi: 10.3390/s150817944.
The aim of this article is to present a new face recognition system based on the fusion of visible and thermal features obtained from the most current local matching descriptors by maximizing face recognition rates through the use of genetic algorithms. The article considers a comparison of the performance of the proposed fusion methodology against five current face recognition methods and classic fusion techniques used commonly in the literature. These were selected by considering their performance in face recognition. The five local matching methods and the proposed fusion methodology are evaluated using the standard visible/thermal database, the Equinox database, along with a new database, the PUCV-VTF, designed for visible-thermal studies in face recognition and described for the first time in this work. The latter is created considering visible and thermal image sensors with different real-world conditions, such as variations in illumination, facial expression, pose, occlusion, etc. The main conclusions of this article are that two variants of the proposed fusion methodology surpass current face recognition methods and the classic fusion techniques reported in the literature, attaining recognition rates of over 97% and 99% for the Equinox and PUCV-VTF databases, respectively. The fusion methodology is very robust to illumination and expression changes, as it combines thermal and visible information efficiently by using genetic algorithms, thus allowing it to choose optimal face areas where one spectrum is more representative than the other.
本文旨在提出一种基于可见光和热成像特征融合的新型人脸识别系统，该特征来自最新的局部匹配描述符，通过使用遗传算法最大化人脸识别率。本文考虑将所提出的融合方法的性能与五种当前的人脸识别方法以及文献中常用的经典融合技术进行比较。这些方法是根据它们在人脸识别中的性能来选择的。使用标准的可见光/热成像数据库Equinox数据库以及一个新的数据库PUCV-VTF对这五种局部匹配方法和所提出的融合方法进行评估，PUCV-VTF数据库是为面部识别中的可见光-热成像研究而设计的，并且在本文中首次进行描述。后者是考虑具有不同现实条件的可见光和热成像图像传感器创建的，例如光照、面部表情、姿势、遮挡等方面的变化。本文的主要结论是，所提出的融合方法的两个变体超越了当前的人脸识别方法以及文献中报道的经典融合技术，在Equinox数据库和PUCV-VTF数据库上分别实现了超过97%和99%的识别率。该融合方法对光照和表情变化具有很强的鲁棒性，因为它通过使用遗传算法有效地结合了热成像和可见光信息，从而能够选择在一个光谱比另一个光谱更具代表性的最佳面部区域。
