Compositional Model-Based Sketch Generator in Facial Entertainment

Zhang, Mingjin; Li, Jie; Wang, Nannan; Gao, Xinbo

doi:10.1109/tcyb.2017.2664499

Cited by 41 publications

(13 citation statements)

References 41 publications

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“…Edge or fog computing could be promising techniques for reducing transmission time during computational offloading in latency-sensitive applications for 5G [22]. However, a lots of research issues (i.e., resource sharing, access with legacy devices, network coexistence among different radio access technologies, propagation and environmental issues, cost and power issues, and network coexistence among different radio access technologies) need to be addressed [21], [23], [24], [25]. To the best of our knowledge, none of the presented schemes deal with the resource allocation for efficient haptic communications in a nanonetwork under the constraints of the system stability, energy consumption, and latency.…”

Section: Related Workmentioning

confidence: 99%

Optimal Haptic Communications Over Nanonetworks for E-Health Systems

Feng

Ali

Iqbal

et al. 2019

IEEE Trans. Ind. Inf.

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Tactile Internet-based nanonetwork is an emerging field that promises a new range of e-health applications, in which human operators can efficiently operate and control devices at the nanoscale for remote-patient treatment. Haptic feedback is inevitable for establishing a link between the operator and unknown in-body environment. However, haptic communications over the terahertz band may incur significant path loss due to molecular absorption. In this paper, we propose an optimization framework for haptic communications over nanonetworks, in which in-body nano-devices transmit haptic information to an operator via the terahertz band. By considering the properties of the terahertz band, we employ Brownian motion to describe the mobility of the nano-devices and develop a time-variant terahertz channel model. Furthermore, based on the developed channel model, we construct a stochastic optimization problem for improving haptic communications under the constraints of system stability, energy consumption, and latency. To solve the formulated non-convex stochastic problem, an improved timevarying particle swarm optimization algorithm is presented, which can deal with the constraints of the problem efficiently by reducing the convergence time significantly. The simulation results validate the theoretical analysis of the proposed system.

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Section: Related Workmentioning

confidence: 99%

Optimal Haptic Communications Over Nanonetworks for E-Health Systems

Feng

Ali

Iqbal

et al. 2019

IEEE Trans. Ind. Inf.

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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. The proposed method can obviously reduce the high frequency loss and has a good performance in sketch image synthesis.…”

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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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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“…Face sketch identification is a challenging computer vision task in criminal investigations. Due to the unavailability of information such as images or video recording in some circumstances, law enforcement agencies are in urgent need of an alternative method to catch suspects [1], [2]. Accordingly, an automatic algorithm is one of the police urgent needs for searching face archives to identify criminal suspects and arrest them within a short period of time.…”

Section: Introductionmentioning

confidence: 99%

SP-Net: A Novel Framework to Identify Composite Sketch

Cheraghi

Lee

2019

IEEE Access

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Identifying composite sketches with digital face photos is an important and challenging task for law enforcement agencies. It has attracted a wide research interest in the face recognition area. In this paper, we present a novel framework that identifies the photo corresponding to a given composite face sketch. A coupled deep convolutional neural network, named Sketch-Photo Net (SP-Net) is proposed, which is fed with a positive or negative photo-sketch pair. In the proposed SP-Net, the customized VGG-Face network is adopted as base model and is followed by two branches, namely S-Net and P-Net, for sketch and photo, respectively. The S-Net and the P-Net are able to learn discriminative features between the sketches and the photos, regardless of the appearance gap by introducing the concept of elastic learning. In other words, to extract the most important features from the input, the network needs to learn the relevant features along with the irrelevant ones. To do so, higher dimension layers are used after the three 512 layers from VGG-FaceNet. Since the network learns representative features, we decrease the dimension of the layers to produce the most representative features. In addition, contrastive loss is employed to discover the coherent visual structures between sketch and photo. Experimental results on E-PRIP face sketch dataset indicate that the proposed network significantly outperforms the state-of-the-art composite sketch identification methods. INDEX TERMSComposite sketch, hand-drawn sketches, convolutional neural network, contrastive loss.

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Compositional Model-Based Sketch Generator in Facial Entertainment

Cited by 41 publications

References 41 publications

Optimal Haptic Communications Over Nanonetworks for E-Health Systems

Optimal Haptic Communications Over Nanonetworks for E-Health Systems

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

SP-Net: A Novel Framework to Identify Composite Sketch

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