An extensive survey on traditional and deep learning-based face sketch synthesis models

Balayesu, N.; Kalluri, Hemantha Kumar

doi:10.1007/s41870-019-00386-8

Cited by 8 publications

(3 citation statements)

References 49 publications

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“…Additionally, the applications and potential research directions for GAN are discussed in different domains. Kalluri et al [21] focused on Face Sketch Synthesis (FSS) for face recognition is discussed, emphasizing deep learning (DL) applications. This comprehensive survey categorizes FSS techniques into data-driven and model-driven methods, detailing the benefits and performance measures.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Framework for Face Sketch Synthesis Using Generative Adversarial Network

LATHA,

VELMURUGAN

2024

Preprint

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Face sketch synthesis phenomenon, a kind of image-image translation, generates synthesized face/sketch with wide range of applications pertaining law enforcement and entertainment to mention few. Despite the compelling results produced by many existing methods of late, there are still challenges due to deformation and blurred effects on facial components resulting in unrealistic face/sketch. To overcome this problem, in this paper, we proposed a novel framework known as Deep Face-Sketch Synthesis Framework (DFSSF). The framework is realized with different building blocks including an algorithm known as Deep Face-Sketch Synthesis for High Perceptual Quality (DFSS-HPQ). The framework is based on the architecture of Generative Adversarial Network (GAN) which exploits facial structures and a novel labelling mechanism. It takes paired inputs compromising of face images and sketches. The framework also considers extraction of GANs with heterogeneity from inputs. Afterwards, they are stacked to obtain additional features that can be effectively used to rectify defects if any. Two algorithms known as Hybrid GAN for Face Sketch Synthesis (HGAN-FSS) and Stacked Hybrid GAN for Face Sketch Synthesis (SHGAN-FSS) are proposed. We used two frequently used datasets namely CUFS and CUFSF having samples collected from 606 and 1194 persons respectively. The proposed framework is built using Python data science platform. Empirical results of the framework are evaluated and compared with traditional face sketch methods, deep learning models and deep learning models based on GANs. The proposed framework showed better performance over the state of the art in presence of different styles, lighting conditions and head poses.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Framework for Face Sketch Synthesis Using Generative Adversarial Network

LATHA,

VELMURUGAN

2024

Preprint

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“…Sketch Generated Structural Similarity Index (SSIM) SketchyGAN [34] 0.58 -Similarity preserving generative adversarial networks SPGAN [35] 0.61 -Super-resolution generative adversarial networks SRGAN [36] 0.64 -Ours (before parameter tuning) 0.63 0.68 Ours (after parameter tuning) 0.65 0.70…”

Section: Ground Truthmentioning

confidence: 99%

Adversarial sketch-photo transformation for enhanced face recognition accuracy: a systematic analysis and evaluation

Shetty Kirimanjeshwara,

Narasimha Prasad

2024

IJECE

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This research provides a strategy for enhancing the precision of face sketch identification through adversarial sketch-photo transformation. The approach uses a generative adversarial network (GAN) to learn to convert sketches into photographs, which may subsequently be utilized to enhance the precision of face sketch identification. The suggested method is evaluated in comparison to state-of-the-art face sketch recognition and synthesis techniques, such as sketchy GAN, similarity-preserving GAN (SPGAN), and super-resolution GAN (SRGAN). Possible domains of use for the proposed adversarial sketch-photo transformation approach include law enforcement, where reliable face sketch recognition is essential for the identification of suspects. The suggested approach can be generalized to various contexts, such as the creation of creative photographs from drawings or the conversion of pictures between modalities. The suggested method outperforms state-of-the-art face sketch recognition and synthesis techniques, confirming the usefulness of adversarial learning in this context. Our method is highly efficient for photo-sketch synthesis, with a structural similarity index (SSIM) of 0.65 on The Chinese University of Hong Kong dataset and 0.70 on the custom-generated dataset.

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“…Face Sketch Synthesis (FSS) plays a central role in both digital social entertainment and criminal investigations for lawenforcement [1,2]. For instance, in public safety applications, it is common that a face photo of a suspect, captured by a surveillance camera, is in low resolution with pose/light variations, occlusion or even worse, it cannot be available due to its fraudulent concealment [3].…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Sami¹,

Daamouche²,

Bengherabi

et al. 2022

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Face Sketch Recognition (FSR) presents a severe challenge to conventional recognition paradigms developed basically to match face photos. This challenge is mainly due to the large texture discrepancy between face sketches, characterized by shape exaggeration, and face photos. In this paper, we propose a training-free synthesized face sketch recognition method based on the rank-level fusion of multiple Image Quality Assessment (IQA) metrics. The advantages of IQA metrics as a recognition engine are combined with the rank-level fusion to boost the final recognition accuracy. By integrating multiple IQA metrics into the face sketch recognition framework, the proposed method simultaneously performs face-sketch matching application and evaluates the performance of face sketch synthesis methods. To test the performance of the recognition framework, five synthesized face sketch methods are used to generate sketches from face photos. We use the Borda count approach to fuse four IQA metrics, namely, structured similarity index metric, feature similarity index metric, visual information fidelity and gradient magnitude similarity deviation at the rank-level. Experimental results and comparison with the state-of-the-art methods illustrate the competitiveness of the proposed synthesized face sketch recognition framework.

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An extensive survey on traditional and deep learning-based face sketch synthesis models

Cited by 8 publications

References 49 publications

A Deep Learning Framework for Face Sketch Synthesis Using Generative Adversarial Network

A Deep Learning Framework for Face Sketch Synthesis Using Generative Adversarial Network

Adversarial sketch-photo transformation for enhanced face recognition accuracy: a systematic analysis and evaluation

Bulletin of the Polish Academy of Sciences: Technical Sciences

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