Face detection using deep learning: An improved faster RCNN approach

Sun, Xudong; Wu, Pengcheng; Hoi, Steven C. H.

doi:10.1016/j.neucom.2018.03.030

Cited by 554 publications

(241 citation statements)

References 26 publications

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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%

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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Section: Related Workmentioning

confidence: 99%

Feature agglomeration networks for single stage face detection

Zhang

Hoi

et al. 2020

Neurocomputing

Self Cite

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“…This effect could be associated with hard negative mining, which has been a successful strategy to improve neural network performance (e.g. Ogier Du Terrail & Jurie, ; Sun, Wu, & Hoi, ). Data augmentation is also important for ensuring robustness (Goodfellow, Bengio, & Courville, ), particularly with small datasets.…”

Section: Discussionmentioning

confidence: 99%

CORIGAN: Assessing multiple species and interactions within images

et al. 2019

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Images are resourceful data for ecologists and can provide a more complete information than other methods to study biodiversity and the interactions between species. Automated image analysis however often relies on extensive datasets, not implementable by small research teams. We are here proposing an object detection method that allows the analysis of high‐resolution images containing many animals interacting in a small dataset. We developed an image analysis pipeline named ‘CORIGAN’ to extract the characteristics of animal communities. CORIGAN is based on the YOLOv3 model as the core of object detection. To illustrate potential applications, we use images collected during a sentinel prey experiment. Our pipeline can be used to detect, count and study the physical interactions between various animals. On our example dataset, the model reaches 86.6% precision and 88.9% recall at the species level or even at the caste level for ants. The training set required fewer than 10 hr of labelling. Based on the pipeline output, it was possible to build the trophic and non‐trophic interactions network describing the studied community. CORIGAN relies on generic properties of the detected animals and can be used for a wide range of studies and supports. Here, we study invertebrates on high‐resolution images, but the same processing can be transferred for the study of larger animals on satellite or aircraft images.

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“…Although important progress has been made in recent years in face detection [7,46,48,42], some level of inaccuracy is inevitable in a practical setting. So, it is important to assess the robustness of the proposed method to coarser initialisation.…”

Section: Lfwmentioning

confidence: 99%

Jointly Aligning Millions of Images With Deep Penalised Reconstruction Congealing

Annunziata¹,

Sagonas²,

Calì³

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Extrapolating fine-grained pixel-level correspondences in a fully unsupervised manner from a large set of misaligned images can benefit several computer vision and graphics problems, e.g. co-segmentation, super-resolution, image edit propagation, structure-from-motion, and 3D reconstruction. Several joint image alignment and congealing techniques have been proposed to tackle this problem, but robustness to initialisation, ability to scale to large datasets, and alignment accuracy seem to hamper their wide applicability. To overcome these limitations, we propose an unsupervised joint alignment method leveraging a densely fused spatial transformer network to estimate the warping parameters for each image and a low-capacity auto-encoder whose reconstruction error is used as an auxiliary measure of joint alignment. Experimental results on digits from multiple versions of MNIST (i.e., original, perturbed, affNIST and infiMNIST) and faces from LFW, show that our approach is capable of aligning millions of images with high accuracy and robustness to different levels and types of perturbation. Moreover, qualitative and quantitative results suggest that the proposed method outperforms state-of-theart approaches both in terms of alignment quality and robustness to initialisation.

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Face detection using deep learning: An improved faster RCNN approach

Cited by 554 publications

References 26 publications

Feature agglomeration networks for single stage face detection

Feature agglomeration networks for single stage face detection

CORIGAN: Assessing multiple species and interactions within images

Jointly Aligning Millions of Images With Deep Penalised Reconstruction Congealing

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