S^3FD: Single Shot Scale-Invariant Face Detector

Zhang, Shifeng; Zhu, Xiangyu; Lei, Zhen; Shi, Hailin; Wang, Xiaobo; Li, Stan Z.

doi:10.1109/iccv.2017.30

Cited by 473 publications

(570 citation statements)

References 59 publications

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“…The recent advances in face recognition (e.g. [38]) and facial landmark detection (e.g. [4,29]) have led to large image datasets with identity labels and 2D face landmarks.…”

Section: Ringnetmentioning

confidence: 99%

Learning to Regress 3D Face Shape and Expression From an Image Without 3D Supervision

Sanyal

Bolkart

Feng

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

281

286

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Figure 1: Without 3D supervision, RingNet learns a mapping from the pixels of a single image to the 3D facial parameters of the FLAME model [21]. Top: Images are from the CelebA dataset [22]. Bottom: estimated shape, pose and expression. AbstractThe estimation of 3D face shape from a single image must be robust to variations in lighting, head pose, expression, facial hair, makeup, and occlusions. Robustness requires a large training set of in-the-wild images, which by construction, lack ground truth 3D shape. To train a network without any 2D-to-3D supervision, we present RingNet, which learns to compute 3D face shape from a single image. Our key observation is that an individual's face shape is constant across images, regardless of expression, pose, lighting, etc. RingNet leverages multiple images of a person and automatically detected 2D face features. It uses a novel loss that encourages the face shape to be similar when the identity is the same and different for different people. We achieve invariance to expression by representing the face using the FLAME model. Once trained, our method takes a single image and outputs the parameters of FLAME, which can be readily animated. Additionally we create a new database of faces "not quite in-the-wild" (NoW) with 3D head scans and high-resolution images of the subjects in a wide variety of conditions. We evaluate publicly available methods and find that RingNet is more accurate than methods that use 3D supervision. The dataset, model, and results are available for research purposes at

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“…The recent advances in face recognition (e.g. [38]) and facial landmark detection (e.g. [4,29]) have led to large image datasets with identity labels and 2D face landmarks.…”

Section: Ringnetmentioning

confidence: 99%

Learning to Regress 3D Face Shape and Expression From an Image Without 3D Supervision

Sanyal

Bolkart

Feng

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

281

286

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“…Generic Object Detection. As a special case of generic object detection, many face detectors inherit successful techniques for generic object detection [28,12,18]. There are two major categories of Region-based CNN variants for object detection: (i) two-stage detection systems where proposals are generated in the first stage and further classified in the second stage; and (ii) single-stage detection systems where the object detection and classification are performed simultaneously from the feature maps without a separate proposal generation stage.…”

Section: Related Workmentioning

confidence: 99%

“…Single-shot multi-box detector (SSD) [12] applies multi-scale feature representations for detecting different scales and thus only a single pass is required. S3FD [28] inherits SSD framework with carefully designed scale-aware anchors. However, S3FD shares the same limitation of SSD, where each feature is used alone for prediction and as a consequence, high-resolution features may fail to provide robust prediction due to the weak semantics.…”

Section: Related Workmentioning

confidence: 99%

Feature agglomeration networks for single stage face detection

Zhang

Hoi

et al. 2020

Neurocomputing

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Recent years have witnessed promising results of face detection using deep learning. Despite making remarkable progresses, face detection in the wild remains an open research challenge especially when detecting faces at vastly different scales and characteristics. In this paper, we propose a novel simple yet effective framework of "Feature Agglomeration Networks" (FANet) to build a new single stage face detector, which not only achieves state-of-the-art performance but also runs efficiently. As inspired by Feature Pyramid Networks (FPN) [11], the key idea of our framework is to exploit inherent multi-scale features of a single convolutional neural network by aggregating higher-level semantic feature maps of different scales as contextual cues to augment lower-level feature maps via a hierarchical agglomeration manner at marginal extra computation cost. We further propose a Hierarchical Loss to effectively train the FANet model. We evaluate the proposed FANet detector on several public face detection benchmarks, including PASCAL face, FDDB and WIDER FACE datasets and achieved state-of-the-art results. Our detector can run in real time for VGA-resolution images on GPU.

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“…The final detection results are merged from the predictions of each image. Zhang et al [87,92] used a more extensive image pyramid structure to handle different scale objects. They resized the testing image to different scales and each scale was responsible for a certain scale range of objects.…”

Section: Othersmentioning

confidence: 99%

“…gies. Many efforts have been made to improve the design choice of anchors.Zhang et al proposed Single Shot Scaleinvariant Face Detector (S3FD)[87] based on SSD with carefully designed anchors to match the objects. According to the effective receptive field[88] of different feature maps, different anchor priors were designed.…”

mentioning

confidence: 99%

Recent advances in deep learning for object detection

2020

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Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades.Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc.Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning.

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S^3FD: Single Shot Scale-Invariant Face Detector

Cited by 473 publications

References 59 publications

Learning to Regress 3D Face Shape and Expression From an Image Without 3D Supervision

Learning to Regress 3D Face Shape and Expression From an Image Without 3D Supervision

Feature agglomeration networks for single stage face detection

Recent advances in deep learning for object detection

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