An improved multi-scale face detection using convolutional neural network

Mliki, Hazar; Dammak, Sahar; Fendri, Emna

doi:10.1007/s11760-020-01680-w

Cited by 37 publications

(12 citation statements)

References 17 publications

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“…They combined at multiple scales both local and global features. The experimental results show that the suggested method works well on multi-scale face detection problems [23]. Masud et al suggested a tree-based model which is using deep model for automatic face recognition in a cloud environment which is computationally less expensive.…”

Section: Literature Reviewmentioning

confidence: 94%

CNN-based Gender Prediction in Uncontrolled Environments

Yildiz

Güneş

Baş

2021

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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With the increasing amount of data produced and collected, the use of artificial intelligence technologies has become inevitable. By using deep learning techniques from these technologies, high performance can be achieved in tasks such as classification and face analysis in the fields of image processing and computer vision. In this study, Convolutional Neural Networks (CNN), one of the deep learning algorithms, was used. The model created with this algorithm was trained with facial images and gender prediction was made. As a result of the experiments, 93.71% success rate was achieved on the VGGFace2 data set and 85.52% success rate on the Adience data set. The aim of the study is to classify low-resolution images with high accuracy.

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Section: Literature Reviewmentioning

confidence: 94%

CNN-based Gender Prediction in Uncontrolled Environments

Yildiz

Güneş

Baş

2021

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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“…According to definition in [55], objects with size smaller than 32 × 32 pixels are considered as small objects. Current state-of-the-art scale-invariant [17,[56][57][58] and scale-variant [32,38,59] models cannot handle such wide range of scales and small sizes of human heads.…”

Section: Senet: Scale Estimation Networkmentioning

confidence: 99%

Multi-Scale Person Localization With Multi-Stage Deep Sequential Framework

Khan¹,

Basalamah²

2021

IJCIS

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Person detection in real videos and images is a classical research problem in computer vision. Person detection is a nontrivial problem that offers many challenges due to several nuisances that commonly observed in natural videos. Among these, scale is the main challenging problem in various object detection tasks. To solve the scale problem, we propose a framework that estimates the scales of person's heads, as we argue that head is the only visible part in complex scenes. we propose a head detection framework that explicitly handles head scales. The framework consists of two sequential networks: (1) scale estimation network (SENet) and (2) head detection network. SENet predicts the distribution of scales from the input image in the form of histogram. Then the scale histogram adjust anchor scale set of region proposal network that generates object proposals. These objects proposals are then classified into two classes, that is, head and background by the detection network. We evaluate proposed framework on three challenging benchmark datasets. Experiment results show that proposed framework achieves state-of-the-art performance.

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“…ResNet-50: [10] has been successfully used in various pattern recognition tasks such as face and pedestrian detection [22]. It containing 50 layers trained on the Im-ageNet dataset.…”

Section: Feature Extraction Layermentioning

confidence: 99%

Efficient Masked Face Recognition Method during the COVID-19 Pandemic

Hariri

2021

Preprint

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The coronavirus disease (COVID-19) is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on occlusion removal and deep learning-based features in order to address the problem of the masked face recognition process. The first step is to remove the masked face region. Next, we apply three pre-trained deep Convolutional Neural Networks (CNN) namely, VGG-16, AlexNet, and ResNet-50, and use them to extract deep features from the obtained regions (mostly eyes and forehead regions). The Bag-of-features paradigm is then applied to the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance compared to other state-of-the-art methods.

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An improved multi-scale face detection using convolutional neural network

Cited by 37 publications

References 17 publications

CNN-based Gender Prediction in Uncontrolled Environments

CNN-based Gender Prediction in Uncontrolled Environments

Multi-Scale Person Localization With Multi-Stage Deep Sequential Framework

Efficient Masked Face Recognition Method during the COVID-19 Pandemic

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