Deep Recurrent Regression with a Heatmap Coupling Module for Facial Landmarks Detection

Hassaballah, M.; Salem, E.H.; Ali, Abdel-Magid M.; Mahmoud, Mountasser M.M.

doi:10.1007/s12559-022-10065-9

Cited by 5 publications

(2 citation statements)

References 69 publications

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“…With the development of deep learning, there have been a number of studies that achieved the success in facial landmarks detection tasks [23][24][25][26]. Facial landmarks served as a sparse representation of key features of a face, and facial landmarks detection is used to facial analysis tasks such as face recognition [27][28][29], FER [30][31][32][33], and face detection [34].…”

Section: Facial Landmarks In Fermentioning

confidence: 99%

Facial Expression Recognition with Enhanced Relation-Aware Attention and Cross-Feature Fusion transformer

DONG,

Wang,

et al. 2024

Preprint

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Face expression recognition(FER) is an important research branch in the field of the computer vision neighborhood. Three prevalent problems in FER tasks that severely impact recognition rates are inter-class similarity, intra-class differences, and facial occlusion issues. Although there have been studies that address some of these issues, none of them can adequately address all three issues in a unified framework. In this paper, we propose a novel dual-branch structure of enhanced relation-aware attention and cross-feature fusion transformer network to comprehensively solve all three issues. Specifically, we design the Enhanced Relation-Aware Attention module to maximize the exploration of more local expression features. At the same time, the Transformer Perceptual Encoder module is adopted to establishing the contextual relationship between individual patches under global information. This greatly alleviates the inter-class similarity problem and the facial occlusion and facial pose transformation problems. On the basis of a dual branch structure, we extract facial image features using facial landmarks features to guide them and design Cross-Feature Fusion Transformer module to deeply cross-fuse two different semantic features. Experiments are performed and results show that our method can greatly alleviated intra-class difference problem with comparison of several traditional methods on three commonly used datasets.

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Section: Facial Landmarks In Fermentioning

confidence: 99%

Facial Expression Recognition with Enhanced Relation-Aware Attention and Cross-Feature Fusion transformer

DONG,

Wang,

et al. 2024

Preprint

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“…To obtain an efficient post-processing-free iris-localization solution and achieve a more accurate and robust iris localization on iris images captured in a non-cooperative environment, we propose a center-based iris localization and segmentation method and make localization rely on a feature map other than the iris mask (see Figure 3 d). Inspired by the use of heat-map for face key point detection [ 24 ], we regard the inner/outer circles as two objects to locate. We first locate the double center and regress the radius thereafter based on the center.…”

Section: Related Researchmentioning

confidence: 99%

Double-Center-Based Iris Localization and Segmentation in Cooperative Environment with Visible Illumination

Feng

2023

Sensors

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Iris recognition has been considered as one of the most accurate and reliable biometric technologies, and it is widely used in security applications. Iris segmentation and iris localization, as important preprocessing tasks for iris biometrics, jointly determine the valid iris part of the input eye image; however, iris images that have been captured in user non-cooperative and visible illumination environments often suffer from adverse noise (e.g., light reflection, blurring, and glasses occlusion), which challenges many existing segmentation-based parameter-fitting localization methods. To address this problem, we propose a novel double-center-based end-to-end iris localization and segmentation network. Different from many previous iris localization methods, which use massive post-process methods (e.g., integro-differential operator-based or circular Hough transforms-based) on iris or contour mask to fit the inner and outer circles, our method directly predicts the inner and outer circles of the iris on the feature map. In our method, an anchor-free center-based double-circle iris-localization network and an iris mask segmentation module are designed to directly detect the circle boundary of the pupil and iris, and segment the iris region in an end-to-end framework. To facilitate efficient training, we propose a concentric sampling strategy according to the center distribution of the inner and outer iris circles. Extensive experiments on the four challenging iris data sets show that our method achieves excellent iris-localization performance; in particular, it achieves 84.02% box IoU and 89.15% mask IoU on NICE-II. On the three sub-datasets of MICHE, our method achieves 74.06% average box IoU, surpassing the existing methods by 4.64%.

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Robust face alignment via adaptive attention-based graph convolutional network

Liang

Líu

Huan

et al. 2023

Neural Comput & Applic

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Deep Recurrent Regression with a Heatmap Coupling Module for Facial Landmarks Detection

Cited by 5 publications

References 69 publications

Facial Expression Recognition with Enhanced Relation-Aware Attention and Cross-Feature Fusion transformer

Facial Expression Recognition with Enhanced Relation-Aware Attention and Cross-Feature Fusion transformer

Double-Center-Based Iris Localization and Segmentation in Cooperative Environment with Visible Illumination

Robust face alignment via adaptive attention-based graph convolutional network

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