Frontal Face Generation from Multiple Pose-Variant Faces with CGAN in Real-World Surveillance Scene

Chen, Zhu-Liang; He, Qianhua; Pang, Wenfeng; Li, Yanxiong

doi:10.1109/icassp.2018.8462648

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…Contrast of face generation effect of different models Input HPEN [25] TP_GAN [55] DR_GAN [58] FF_GAN [74] Hang [77] F I G U R E Input TP_GAN [55] Hang [77] Figure 13 shows a visualization of frontal faces generated by some of the models based on the LFW dataset. Although HPEN tries to fill in the missing parts with symmetric priors, it produces large artifacts when larger poses appear.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

“…Chen et al 58 proposed a method for face recognition in video surveillance scenes based on the use of a conditional generative adversarial network (cGAN). This method can input multiple faces with varying poses from the video.…”

Section: Face Frontalization Based On Deep Learningmentioning

confidence: 99%

“…As shown in Figure8, DR-GAN is based on the CGAN network and adds some new features, including an encoder-decoder structure generator, pose code, and pose classification in the discriminator (identity representation) and an integrated multi-image fusion scheme. DR_GAN can learn a face feature that has nothing to do with posture, and separate the pose information of the face from the feature, allowing the feature extraction used for face recognition to be applied toward the recognition of various angles of the face.Chen et al58 proposed a method for face recognition in video surveillance scenes based on the use of a conditional generative adversarial network (cGAN). This method can input multiple faces with varying poses from the video.…”

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confidence: 99%

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Multi‐view frontal face image generation: A survey

Ning

Nan

et al. 2020

Concurrency and Computation

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Face images from different perspectives reduce the accuracy of face recognition, and the generation of frontal face images is an important research topic in the field of face recognition. To understand the development of frontal face generation models and grasp the current research hotspots and trends, existing methods based on 3D models, deep learning, and hybrid models are summarized, and the current commonly used face generation methods are introduced. Dataset, and compare the performance of existing models through experiments. The purpose of this paper is to fundamentally understand the advantages of existing frontal face generation, sort out the key issues of such generation, and look toward future development trends.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Section: Face Frontalization Based On Deep Learningmentioning

confidence: 99%

mentioning

confidence: 99%

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Multi‐view frontal face image generation: A survey

Ning

Nan

et al. 2020

Concurrency and Computation

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“…Conditional generative adversarial networks (cGAN) ( Mirza and Osindero, 2014 ) has been used for face image generation with conditional constrains ( Chen et al, 2018 ; Lu et al, 2018 ; Bi et al, 2019 ; Deng et al, 2020 ; Heo et al, 2021 ). Compared to traditional GAN ( Goodfellow et al, 2020 ), it allows use of additional information as latent variable input to constrain the image generation process.…”

Section: Methodsmentioning

confidence: 99%

Reconstruction of perceived face images from brain activities based on multi-attribute constraints

2022

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Reconstruction of perceived faces from brain signals is a hot topic in brain decoding and an important application in the field of brain-computer interfaces. Existing methods do not fully consider the multiple facial attributes represented in face images, and their different activity patterns at multiple brain regions are often ignored, which causes the reconstruction performance very poor. In the current study, we propose an algorithmic framework that efficiently combines multiple face-selective brain regions for precise multi-attribute perceived face reconstruction. Our framework consists of three modules: a multi-task deep learning network (MTDLN), which is developed to simultaneously extract the multi-dimensional face features attributed to facial expression, identity and gender from one single face image, a set of linear regressions (LR), which is built to map the relationship between the multi-dimensional face features and the brain signals from multiple brain regions, and a multi-conditional generative adversarial network (mcGAN), which is used to generate the perceived face images constrained by the predicted multi-dimensional face features. We conduct extensive fMRI experiments to evaluate the reconstruction performance of our framework both subjectively and objectively. The results show that, compared with the traditional methods, our proposed framework better characterizes the multi-attribute face features in a face image, better predicts the face features from brain signals, and achieves better reconstruction performance of both seen and unseen face images in both visual effects and quantitative assessment. Moreover, besides the state-of-the-art intra-subject reconstruction performance, our proposed framework can also realize inter-subject face reconstruction to a certain extent.

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“…Conditioning is a general-purpose operation and can be used for different tasks, e.g., conditional image generation [11,12] and cross-modality distillation [13]. The most commonly used approach in conditional GANs is concatenation.…”

Section: Overview Of Conditioning Methodsmentioning

confidence: 99%

Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks

Mao

Chen

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The task of Language-Based Image Editing (LBIE) aims at generating a target image by editing the source image based on the given language description. The main challenge of LBIE is to disentangle the semantics in image and text and then combine them to generate realistic images. Therefore, the editing performance is heavily dependent on the learned representation. In this work, conditional generative adversarial network (cGAN) is utilized for LBIE. We find that existing conditioning methods in cGAN lack of representation power as they cannot learn the second-order correlation between two conditioning vectors. To solve this problem, we propose an improved conditional layer named Bilinear Residual Layer (BRL) to learning more powerful representations for LBIE task. Qualitative and quantitative comparisons demonstrate that our method can generate images with higher quality when compared to previous LBIE techniques.

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Frontal Face Generation from Multiple Pose-Variant Faces with CGAN in Real-World Surveillance Scene

Cited by 9 publications

References 11 publications

Multi‐view frontal face image generation: A survey

Multi‐view frontal face image generation: A survey

Reconstruction of perceived face images from brain activities based on multi-attribute constraints

Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks

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