Mixed attention hourglass network for robust face alignment

Zou, Yang; Shao, Xueguang; Wan, Jun; Gao, Rong; Lai, Zhihui

doi:10.1007/s13042-021-01424-3

Cited by 4 publications

(16 citation statements)

References 54 publications

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“…Table 2 shows the NMEs of the various algorithms, where the mean error is normalized by inter‐pupil distance and inter‐ocular distance. These data are from the original paper, including RCPR [12], LBF [29], TCDCN [30], CFSS [31], MDM [17], DCFE [18], SAN [32], LAB [13], Wing [20], LUVLi [24], FCDN [7], SHN‐GCN [8], 2‐SCRM [15], ASMNet [21], CCNN [25], PIPNet [33] and MAttHG [9].…”

Section: Methodsmentioning

confidence: 99%

“…Figure 7 shows the localization results of PGHN, 2‐SCRM [15], MAttHG [9] and LBF [29] on the 300 W dataset. The first row of the figure shows the ground truth, and the second to fifth rows show the localization results for PGHN, MAttHG, 2‐SCRM and LBF, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The loss function combined with the adaptive weight can solve the problem of unbalanced training samples, and increase detection robustness in the case of large head pose changes. Meanwhile, the Euler angles information extracted in this paper is obtained by calculating only six keypoints (left eye corner, right eye corner, nose tip, left mouth corner, right mouth corner and lower jaw), which is more computationally efficient compared to the 11 points in the traditional algorithm [9]. Next, we introduce the process of calculating them: (1) Define a standard frontal face in advance, and fix six landmarks on the face plane as a reference for all training faces.…”

Section: Probability‐guided Hourglass Networkmentioning

confidence: 99%

“…Zou et al. [9] introduce a mixed attention hourglass network (MAttHG) to extract discriminative representations from the relative features. Ma et al.…”

Section: Introductionmentioning

confidence: 99%

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Robust facial landmark detection by probability‐guided hourglass network

Liang

Líu

et al. 2023

IET Image Processing

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The absence of local features and global shape constraints severely limits the performance of the hourglass network for facial landmark detection in unconstrained environments. Moreover, diverse feature types and scales may result in low accuracy. This paper proposes a probability-guided hourglass network to enhance the shape constraints for robust facial landmark detection. Firstly, a multi-scale pre-processing module is designed to extract features at different scales. Secondly, based on the heatmaps generated by the stacked hourglass network, the coarse localizations are obtained, while the probability maps are generated with local features. Finally, a probability-based boundary regression method is proposed and the hausdorff distance is modified as the loss function to constrain the feature shape. Adaptive weights are also added to the loss function, which can help relieve the data imbalance problem. Subjective and objective experimental results on the challenging datasets show that this method outperforms the state-of-the-arts on unconstrained conditions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Probability‐guided Hourglass Networkmentioning

confidence: 99%

“…Zou et al. [9] introduce a mixed attention hourglass network (MAttHG) to extract discriminative representations from the relative features. Ma et al.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust facial landmark detection by probability‐guided hourglass network

Liang

Líu

et al. 2023

IET Image Processing

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“…Vision attention mechanism enables human to quickly acquire critical information when reading complicated information, thereby it greatly improves the efficiency and accuracy of visual information processing. Attention mechanism in deep learning deriving from the attention thinking mode of human beings has achieved excellent performance in many tasks [14,15,16,17,18,19,20,21]. Our principal contributions are as follows:…”

Section: Introductionmentioning

confidence: 99%

HSCA-Net: A Hybrid Spatial-Channel Attention Network in Multi-Scale Feature Pyramid for Document Layout Analysis

Zhang

Shi

et al. 2022

JAIT

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Document images often contain various page components and complex logical structures, which makes document layout analysis task challenging. For most deep learning based document layout analysis methods, convolutional neural networks (CNNs) are adopted as the image feature extraction networks. In this paper, a hybrid spatial-channel attention network (HSCA-Net) is proposed to improve feature extraction capability by exerting attention mechanism to explore more salient properties within document pages. The HSCA-Net contains two modules: spatial attention module (SAM) and channel attention module (CAM). They are embedded in the multi-scale feature network by lateral attention connection. SAM extracts contextual information with learning offset in spatial dimension and CAM performs feature recalibration by focusing more on feature channels with important contents. The lateral attention connection is to incorporate SAM and CAM into multi-scale feature pyramid network and retain more of the original feature information. The effectiveness and adaptability of HSCA-Net are evaluated through multiple experiments on publicly available datasets PubLayNet, ICDAR-POD and Article Regions. The mAP on these datasets is as high as 0.940,0.939 and 0.967 respectively, which demonstrate that our HSCA-Net achieves competitive results on document layout analysis task. Text.

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