Deep Convolutional Neural Network in Deformable Part Model for Face Detection

Deformable Part Models (DPM) [1] and Convolutional Neural Network (CNN) are state-of-the-art approaches in object detection. While DPM makes use of the general structure between parts and root models, CNN uses all information of input to create meaningful features. These two...

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Main Author: Nguyen, Dinh Luan
Other Authors: Advanced Technologies for IoT Applications
Format: Article
Language:English
Published: 2019
Online Access:http://repository.vnu.edu.vn/handle/VNU_123/67090
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Institution: Vietnam National University, Hanoi
Language: English
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spelling oai:112.137.131.14:VNU_123-670902019-09-04T08:18:37Z Deep Convolutional Neural Network in Deformable Part Model for Face Detection Nguyen, Dinh Luan Advanced Technologies for IoT Applications Deformable Part Models (DPM) [1] and Convolutional Neural Network (CNN) are state-of-the-art approaches in object detection. While DPM makes use of the general structure between parts and root models, CNN uses all information of input to create meaningful features. These two types of characteristics are necessary for face detection. Experimental results show that our method surpasses the highest result of existing methods for face detection on the standard dataset with 87.06% in true positive rate at 1000 number false positive images. Our method sheds a light in face detection which is commonly regarded as a saturated area 2019-09-04T08:18:37Z 2019-09-04T08:18:37Z 2017 Article Nguyen, D. L. (2017). Deep Convolutional Neural Network in Deformable Part Model for Face Detection. Advanced Technologies for IoT Applications. http://repository.vnu.edu.vn/handle/VNU_123/67090 en application/pdf
institution Vietnam National University, Hanoi
building VNU Library & Information Center
country Vietnam
collection VNU Digital Repository
language English
description Deformable Part Models (DPM) [1] and Convolutional Neural Network (CNN) are state-of-the-art approaches in object detection. While DPM makes use of the general structure between parts and root models, CNN uses all information of input to create meaningful features. These two types of characteristics are necessary for face detection. Experimental results show that our method surpasses the highest result of existing methods for face detection on the standard dataset with 87.06% in true positive rate at 1000 number false positive images. Our method sheds a light in face detection which is commonly regarded as a saturated area
author2 Advanced Technologies for IoT Applications
author_facet Advanced Technologies for IoT Applications
Nguyen, Dinh Luan
format Article
author Nguyen, Dinh Luan
spellingShingle Nguyen, Dinh Luan
Deep Convolutional Neural Network in Deformable Part Model for Face Detection
author_sort Nguyen, Dinh Luan
title Deep Convolutional Neural Network in Deformable Part Model for Face Detection
title_short Deep Convolutional Neural Network in Deformable Part Model for Face Detection
title_full Deep Convolutional Neural Network in Deformable Part Model for Face Detection
title_fullStr Deep Convolutional Neural Network in Deformable Part Model for Face Detection
title_full_unstemmed Deep Convolutional Neural Network in Deformable Part Model for Face Detection
title_sort deep convolutional neural network in deformable part model for face detection
publishDate 2019
url http://repository.vnu.edu.vn/handle/VNU_123/67090
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