Deformable part-based robust face detection under occlusion by using face decomposition into face components

Marcetic, Darijan; Ribarić, Slobodan

doi:10.1109/mipro.2016.7522352

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…DPM performs well in terms of detecting various shapes of faces, as it detects faces efficiently in a real-time environment [75]. Additionally, it easily detects faces with various poses and can work with variations caused by different viewpoints and illuminations [76]. However, DPM faces difficulties, such as speed bottleneck or slowness [77], and has issues in extending to new object or face categories.…”

Section: Deformable Part Model (Dpm)mentioning

confidence: 99%

Human Face Detection Techniques: A Comprehensive Review and Future Research Directions

et al. 2021

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Face detection, which is an effortless task for humans, is complex to perform on machines. The recent veer proliferation of computational resources is paving the way for frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is little attention paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. First, we explore a wide variety of the available face detection algorithms in five steps, including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all-inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of the neural network. We present detailed comparisons among the algorithms in all-inclusive and under sub-branches. We provide the strengths and limitations of these algorithms and a novel literature survey that includes their use besides face detection.

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Section: Deformable Part Model (Dpm)mentioning

confidence: 99%

Human Face Detection Techniques: A Comprehensive Review and Future Research Directions

et al. 2021

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“…Study [40,43] tested Caltech database. Study [47] tested with XM2VTS contains occlusion faces. Study [5,14,44,45,48] tested and training from MIT+CMU database.…”

Section: Related Workmentioning

confidence: 99%

The Utilization of Feature based Viola-Jones Method for Face Detection in Invariant Rotation

Soon¹,

Basari²,

Ibrahim³

et al. 2018

ijacsa

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Faces in an image consists of complex structures in object detection. The components of a face, which includes the eyes, nose and mouth of a person differs from that of ordinary objects, thus making face detecting a complex process. Some of the challenges encounter posed in face detection of unconstrained images includes background variation, pose variation, facial expression, occlusion and noise. Current research of Viola-Jones (V-J) face detection is limited to only 45 degrees in-plane rotation. This paper proposes only one technique for the V-J detection face in unconstrained images, which V-J face detection with invariant rotation. The technique begins by rotating the given image file with each step 30 degrees until 360 degrees. Each step of adding 30 degrees from origin, V-J face detection is applied, which covers more angles of a rotated face in unconstrained images. Robust detection in rotation invariant used in the above techniques will aid in the detecting of rotated faces in images. The images that have been utilized for testing and evaluation in this paper are from CMU dataset with 12 rotations on each image. Therefore, there are 12 test patterns generated. These images have been measured through the correct detection rate, true positive and false positive. This paper shows that the proposed V-J face detection technique in unconstrained images have the ability to detect rotated faces with high accuracy in correct detection rate. To summarize, V-J face detection in unconstrained images with proposed variation of rotation is the method utilized in this paper. This proposed enhancement improves the current V-J face detection method and further increase the accuracy of face detection in unconstrained images.

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“…Although this method uses morphological methods to remove background interference, its accuracy cannot meet the demand and it is difficult to detect the obscured faces. Mathias et al (Mathias et al (2014)) and Marčetić et al (Marčetić and Ribarić (2016)) proposed DPMbased face detection methods, respectively, which effectively detect distorted, multi-posed, and obscured faces, but the speed and robustness is poor. Owusu et al (Owusu et al (2019)) extracted facial features by the Haar technique.…”

Section: Introductionmentioning

confidence: 99%

Face Detection Based on Cascaded Multivariate Branches Convolutional Neural Networks

Wang

Shi

2023

Preprint

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To improve MTCNN’s face detection accuracy, this paper proposes multivariate branches block (MBB) and dilated convolutional block (DCB), and we design a cascaded multivariate branch convolutional neural network model(MB-MTCNN) based on these two structural blocks. First, the model consists of three subnetworks, and we replace the first convolutional block of each subnetwork with a dilated convolutional block. Second, replace the second convolutional block of the second and third subnetworks with MBB. In addition, we add a BN layer after each convolutional layer to accelerate the training speed of the model. Finally, we introduce a structural re-parameterization method to reduce the model’s overhead in inference. Experimental results show that our model increases the True Positive Rate (TPR) by 4.48% on the FDDB datasets, improves the average accuracy by 8.08%, 8.65%, and 10.29% on the three levels of validation sets of Wider Face datasets, 8.31% and 2.08% on the AFW and CelebA datasets, respectively. Moreover, based on structural re-parameterization, the model parameters during inference are 14.68% lower than training. This model is suitable for scenarios that require accurate detection of faces, such as crowded stations and traffic arteries.

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Deformable part-based robust face detection under occlusion by using face decomposition into face components

Cited by 10 publications

References 12 publications

Human Face Detection Techniques: A Comprehensive Review and Future Research Directions

Human Face Detection Techniques: A Comprehensive Review and Future Research Directions

The Utilization of Feature based Viola-Jones Method for Face Detection in Invariant Rotation

Face Detection Based on Cascaded Multivariate Branches Convolutional Neural Networks

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