Fast Head Pose Estimation via Rotation-Adaptive Facial Landmark Detection for Video Edge Computation

Wang, Weiwei; Chen, Xiaoyan; Zheng, Shuangwu; Li, Haiqing

doi:10.1109/access.2020.2977729

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…Another drawback of almost all the datasets is the data imbalance issue: the distribution between easy frontal faces and more challenging orientations is heavily unbalanced. Techniques to increase the number of hard faces [195] or to enhance the contribution of hard examples (such as HEM [150]) can be used to alter the data distribution space and [82] Model based DCNN 3 300W-LP, AFLW2000, BIWI, CAS-PEAL, DriveFace 2019 Yang et al [88] DCNN 3 300W-LP, AFLW2000, BIWI 2020 Barra et al [130] Model based 3 AFLW, BIWI, Pointing'04 2020 Cao et al [76] DCNN 3 300W-LP, AFLW2000, BIWI 2020 Dai et al [90] DCNN 3 300W-LP, AFLW2000, BIWI 2020 Dapongy et al [83] Model based 3 300W, 300W-LP, AFLW2000, CelebA, WFLW 2020 Ewaisha et al [168] Multi-task DCNN 3 CAVE 2020 Valle et al [98] Multi-task DCNN 3 300W-LP a,c , AFLW a,c , AFLW2000 a , BIWI a , COFW c , WFLW a,c 2020 Wang et al [182] PnP Model based 3 300W, AFLW2000 2020 Zhang et al [183] DCNN 3 300W-LP, AFLW2000, BIWI 2020 Zhang et al [167] Multi-task DCNN 3 AFLW a,b,c 2020 Zhou et al [7] DCNN 3 300W-LP, AFLW2000, BIWI, CMU Panoptic 2021 Albiero et al [166] Multi-task DCNN 3 300W-LP a , AFLW2000 a , BIWI a , WIDER Multi-task DCNN + ASM 3 300W a,b , WFLW a,b 2021 Hu et al [185] DCNN 3 300W-LP, AFLW2000, BIWI 2021 Khan et al [80] Segmentation based Soft-max classifier 3 AFLW, BU, ICT-3DHP, Pointing'04 2021 Liu et al [85] Multi-task DCNN 3 AFLW c , AFLW2000 a , WIDER * 2021 Naina Dhingra [186] DCNN 3 300W-LP, AFLW2000, BIWI 2021 Ruan et al [87] Model based 3DMM + DCNN 3 300W-LP a,c,g , AFLW2000 ⋄• * , Florence g 2021 Sheka et al [91] DCNN 3 300W-LP, AFLW, AFLW2000, BIWI 2021 Viet et al [102] Multi-task DCNN 3 300W-LP a,b , BIWI a,b , CMU Panoptic a,b 2021 Viet et al [69] DCNN 3 300W-LP, AFLW2000, CMU Panoptic, UET-Headpose 2021 Xia et al [99] Multi-task DCNN 3 300W-LP a , 300VW c , WFLW c , WIDER b 2021 Xin et al [187] Model based Graph CNN 3 300W-LP, AFLW2000, BIWI 2021 Wu et al [86] Model based 3DMM + DCNN 3 300W-LP a,c,g , 300VW g , AFLW c , AFLW2000 a,c , Florence g 2022 Cantarini et al [188] Model based D...…”

Section: Datasetsmentioning

confidence: 99%

Deep Learning for Head Pose Estimation: A Survey

Asperti

Filippini

2023

SN COMPUT. SCI.

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Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the field by proposing a classification of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the different ways deep learning techniques have been used in the context of HPE. An in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic.

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Section: Datasetsmentioning

confidence: 99%

Deep Learning for Head Pose Estimation: A Survey

Asperti

Filippini

2023

SN COMPUT. SCI.

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“…B ENEFITING from the rapid development of deep learning and the easy access to a large number of annotated face images, face recognition [1]- [4] has advanced significantly in recent years. Although impressive performance has been achieved on several benchmarking databases, pose variation is still one of the crucial bottlenecks for many practical applications [5], [6]. Facial appearance variations caused by poses are even larger than those caused by different identities [7].…”

Section: Introductionmentioning

confidence: 99%

Dual Encoder-Decoder Based Generative Adversarial Networks for Disentangled Facial Representation Learning

Feng

et al. 2020

IEEE Access

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To learn disentangled representations of facial images, we present a Dual Encoder-Decoder based Generative Adversarial Network (DED-GAN). In the proposed method, both the generator and discriminator are designed with deep encoder-decoder architectures as their backbones. To be more specific, the encoder-decoder structured generator is used to learn a pose disentangled face representation, and the encoder-decoder structured discriminator is tasked to perform real/fake classification, face reconstruction, determining identity and estimating face pose. We further improve the proposed network architecture by minimizing the additional pixel-wise loss defined by the Wasserstein distance at the output of the discriminator so that the adversarial framework can be better trained. Additionally, we consider face pose variation to be continuous, rather than discrete in existing literature, to inject richer pose information into our model. The pose estimation task is formulated as a regression problem, which helps to disentangle identity information from pose variations. The proposed network is evaluated on the tasks of pose-invariant face recognition (PIFR) and face synthesis across poses. An extensive quantitative and qualitative evaluation carried out on several controlled and in-the-wild benchmarking datasets demonstrates the superiority of the proposed DED-GAN method over the state-of-the-art approaches. INDEX TERMS Disentangled representation learning, encoder-decoder, generative adversarial networks, face synthesis, pose invariant face recognition.

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