Anchored Neighborhood Index for Face Sketch Synthesis

Wang, Nannan; Gao, Xinbo; Sun, Leiyu; Li, Jie

doi:10.1109/tcsvt.2017.2709465

Cited by 64 publications

(17 citation statements)

References 23 publications

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“…In recent years, sketch to image or image to sketch translation have been widely used in digital entertainment field and law enforcement [21][22][23][24][25][26][27][28][29]. Zhang et al [21] proposed a face sketch generator based on the compositional model, which can address the problem of generating sketches with over-smoothing effects in existing face sketch synthesis methods.…”

Section: Introductionmentioning

confidence: 99%

“…In existing face sketch synthesis methods, it is difficult to select the accurate neighbor during the face sketch synthesis, the Knearest neighbor (KNN) matching algorithm is always used in existing methods. Wang et al [28] proposed a simple but effective neighbor selection algorithm, which is named anchored neighborhood index (ANI). Experiment results show that the proposed method have a better performance in face sketch quality and face recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Facial attribute-controlled sketch-to-image translation with generative adversarial networks

Guo

2020

J Image Video Proc.

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Due to the rapid development of the generative adversarial networks (GANs) and convolution neural networks (CNN), increasing attention is being paid to face synthesis. In this paper, we address the new and challenging task of facial sketch-to-image synthesis with multiple controllable attributes. To achieve this goal, first, we propose a new attribute classification loss to ensure that the synthesized face image with the facial attributes, which the users desire to have. Second, we employ the reconstruction loss to synthesize the facial texture and structure information. Third, the adversarial loss is used to encourage visual authenticity. By incorporating above losses into a unified framework, our proposed method not only can achieve high-quality sketch-to-image translation, but also allow the users control the facial attributes of synthesized image. Extensive experiments show that user-provided facial attribute information effectively controls the process of facial sketch-to-image translation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Facial attribute-controlled sketch-to-image translation with generative adversarial networks

Guo

2020

J Image Video Proc.

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show abstract

“…If two photo patches are similar, then their sketch patch counterparts are also similar. However, owing to potential misalignment, the reconstruction weight obtained from the test photo patch and selected training photo patches may not be suitable for sketch patches reconstruction [19].…”

Section: Introductionmentioning

confidence: 99%

A Joint Training Model for Face Sketch Synthesis

Wan

Lee

2019

Applied Sciences

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The exemplar-based method is most frequently used in face sketch synthesis because of its efficiency in representing the nonlinear mapping between face photos and sketches. However, the sketches synthesized by existing exemplar-based methods suffer from block artifacts and blur effects. In addition, most exemplar-based methods ignore the training sketches in the weight representation process. To improve synthesis performance, a novel joint training model is proposed in this paper, taking sketches into consideration. First, we construct the joint training photo and sketch by concatenating the original photo and its sketch with a high-pass filtered image of their corresponding sketch. Then, an offline random sampling strategy is adopted for each test photo patch to select the joint training photo and sketch patches in the neighboring region. Finally, a novel locality constraint is designed to calculate the reconstruction weight, allowing the synthesized sketches to have more detailed information. Extensive experimental results on public datasets show the superiority of the proposed joint training model, both from subjective perceptual and the FaceNet-based face recognition objective evaluation, compared to existing state-of-the-art sketch synthesis methods.

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“…If crime happened, and only incomplete information is obtained about the suspect because of the bad quality of monitoring videos, face sketches which drawn by the artists with the description of witnesses are used to identity the possible suspect. The police can shrink the range of suspects by searching the law enforcement face databases or surveillance cameras with the drawn face sketch images [2].…”

Section: Introductionmentioning

confidence: 99%

Deep Feature Representation for Face Sketch Recognition

Wan¹,

Hj²

2019

Adv. sci. technol. eng. syst. j.

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Face sketch recognition aims at matching face sketch images to face photo images. The main challenge lies in modality discrepancy between face photo and sketch images. In this work, we propose a new facial sketch-to-photo recognition approach by adopting VGG-Face deep learning network, with which face images can be represented by compact and highly discriminative feature vectors. Different from existing VGG-Face based methods which directly match face sketches to photos, we firstly transform the gallery photos to sketches for decreasing the modality difference. Experimental results on multiple face photo-sketch datasets indicate the superiority of our method.

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Anchored Neighborhood Index for Face Sketch Synthesis

Cited by 64 publications

References 23 publications

Facial attribute-controlled sketch-to-image translation with generative adversarial networks

Facial attribute-controlled sketch-to-image translation with generative adversarial networks

A Joint Training Model for Face Sketch Synthesis

Deep Feature Representation for Face Sketch Recognition

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