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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| 格式: | Article |
| 語言: | English |
| 出版: |
2019
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| 在線閱讀: | http://repository.vnu.edu.vn/handle/VNU_123/67090 |
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| 機構: | Vietnam National University, Hanoi |
| 語言: | English |
| 總結: | 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 |
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