On Matching Visible to Passive Infrared Face Images Using Image Synthesis & Denoising
On Matching Visible to Passive Infrared Face Images Using Image Synthesis & Denoising
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DOI：
10.1109/fg.2017.129
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发表时间：
2017-05
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通讯作者：
N. Osia;T. Bourlai
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文献类型：
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作者：
N. Osia;T. Bourlai
在不同光谱（即被动红外和可见光）的图像之间进行直接匹配是具有挑战性的，因为每个光谱包含与受试者面部相关的不同信息。在这项工作中，我们研究了在交叉光谱人脸识别系统中使用从热图像合成的可见人脸图像的优点和局限性，反之亦然。为此，我们建议利用典型相关分析（CCA）和流形学习降维（LLE）。这项工作有四个主要贡献。首先，我们使用图像合成框架制定了跨光谱异构人脸匹配问题（可见到被动红外）。其次，从三个不同波段（可见、MWIR和LWIR）收集的原始原始人脸数据集生成一个新的处理数据库，该数据库由两个独立的受控正面人脸子集（VIS-MWIR和VIS-LWIR）组成。在应用特征提取方法之前，使用三种不同的方法对人脸图像进行预处理，构建了该多波段数据库。有：(1)人脸检测，(2)CSU的几何归一化，(3)我们推荐的几何归一化方法。第三，应用合成后图像去噪方法，有助于缓解合成图像中存在的不同噪声模式，并提高实际异构FR场景下基线FR精度（即在图像合成和去噪之前）。最后，进行了广泛的实验研究，以证明交叉光谱匹配在使用我们的图像合成和去噪方法时的可行性和优点。我们的结果还与CSU的面部识别评估系统提供的基准商业匹配器和各种学术匹配器进行了比较。
Performing a direct match between images from different spectra (i.e., passive infrared and visible) is challenging because each spectrum contains different information pertaining to the subject’s face. In this work, we investigate the benefits and limitations of using synthesized visible face images from thermal ones and vice versa in cross-spectral face recognition systems. For this purpose, we propose utilizing canonical correlation analysis (CCA) and manifold learning dimensionality reduction (LLE). There are four primary contributions of this work. First, we formulate the cross-spectral heterogeneous face matching problem (visible to passive IR) using an image synthesis framework. Second, a new processed database composed of two datasets consistent of separate controlled frontal face subsets (VIS-MWIR and VIS-LWIR) is generated from the original, raw face datasets collected in three different bands (visible, MWIR and LWIR). This multi-band database is constructed using three different methods for preprocessing face images before feature extraction methods are applied. There are: (1) face detection, (2) CSU’s geometric normalization, and (3) our recommended geometric normalization method. Third, a post-synthesis image denoising methodology is applied, which helps alleviate different noise patterns present in synthesized images and improve baseline FR accuracy (i.e. before image synthesis and denoising is applied) in practical heterogeneous FR scenarios. Finally, an extensive experimental study is performed to demonstrate the feasibility and benefits of cross-spectral matching when using our image synthesis and denoising approach. Our results are also compared to a baseline commercial matcher and various academic matchers provided by the CSU’s Face Identification Evaluation System.