Hallucinating faces from thermal infrared images

Li, Jun; Hao, Pengwei; Zhang, Chao; Dou, Mingsong

doi:10.1109/icip.2008.4711792

Cited by 36 publications

(22 citation statements)

References 13 publications

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“…1 show that even humans would have difficulty in this recognition task. The only previous method on thermal to visible matching achieved a Rank-1 accuracy of 50.06 percent, but it was evaluated on only 47 subjects in the gallery [17]. By contrast, the Rank-1 accuracy for the proposed P-RS method was computed using a gallery consisting of 10,333 subjects.…”

Section: Resultsmentioning

confidence: 99%

“…The synthesis method by Li et al is the only known method to perform recognition between thermal IR and visible face images [17]. The only method to perform recognition between forensic sketches and visible face images is Klare et al [9], which is also one of two methods, to our knowledge, that has been tested on two different HFR scenarios (viewed sketch and forensic sketch).…”

Section: Related Work 21 Heterogeneous Face Recognitionmentioning

confidence: 99%

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Heterogeneous Face Recognition Using Kernel Prototype Similarities

Klare

Jain

2013

IEEE Trans. Pattern Anal. Mach. Intell.

330

201

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Abstract-Heterogeneous face recognition (HFR) involves matching two face images from alternate imaging modalities, such as an infrared image to a photograph or a sketch to a photograph. Accurate HFR systems are of great value in various applications (e.g., forensics and surveillance), where the gallery databases are populated with photographs (e.g., mug shot or passport photographs) but the probe images are often limited to some alternate modality. A generic HFR framework is proposed in which both probe and gallery images are represented in terms of nonlinear similarities to a collection of prototype face images. The prototype subjects (i.e., the training set) have an image in each modality (probe and gallery), and the similarity of an image is measured against the prototype images from the corresponding modality. The accuracy of this nonlinear prototype representation is improved by projecting the features into a linear discriminant subspace. Random sampling is introduced into the HFR framework to better handle challenges arising from the small sample size problem. The merits of the proposed approach, called prototype random subspace (P-RS), are demonstrated on four different heterogeneous scenarios: 1) near infrared (NIR) to photograph, 2) thermal to photograph, 3) viewed sketch to photograph, and 4) forensic sketch to photograph.

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Section: Resultsmentioning

confidence: 99%

Section: Related Work 21 Heterogeneous Face Recognitionmentioning

confidence: 99%

Heterogeneous Face Recognition Using Kernel Prototype Similarities

Klare

Jain

2013

IEEE Trans. Pattern Anal. Mach. Intell.

330

201

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“…Up to now, many HFR approaches have been proposed, which can be broadly classified into three categories: image synthesis-based methods, common subspace projectionbased methods, and modality invariant feature descriptor-based methods. Image synthesis-based methods [4], [5], [6], [7], [8], [9], [10], [11], [12], [13] transform face images from one modality into another such that they become homogeneous and the conventional face recognition methods can then be applied directly. However, the image synthesis process is a complex problem itself, even more difficult than the recognition task.…”

Section: Introductionmentioning

confidence: 99%

Sparse graphical representation based discriminant analysis for heterogeneous face recognition

et al. 2019

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Face images captured in heterogeneous environments, e.g., sketches generated by the artists or compositegeneration software, photos taken by common cameras and infrared images captured by corresponding infrared imaging devices, usually subject to large texture (i.e., style) differences. This results in heavily degraded performance of conventional face recognition methods in comparison with the performance on images captured in homogeneous environments. In this paper, we propose a novel sparse graphical representation based discriminant analysis (SGR-DA) approach to address aforementioned face recognition in heterogeneous scenarios. An adaptive sparse graphical representation scheme is designed to represent heterogeneous face images, where a Markov networks model is constructed to generate adaptive sparse vectors. To handle the complex facial structure and further improve the discriminability, a spatial partition-based discriminant analysis framework is presented to refine the adaptive sparse vectors for face matching. We conducted experiments on six commonly used heterogeneous face datasets and experimental results illustrate that our proposed SGR-DA approach achieves superior performance in comparison with state-of-the-art methods.

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“…Wang et al [18] introduced a multi-dictionary sparse representation. Li et al [19] proposed a HFR between thermal IR and visible face images using synthesis method.…”

Section: Review Of Literaturementioning

confidence: 99%