Deep Convolutional Neural Network in Deformable Part Models for Face Detection
Deformable Part Models and Convolutional Neural Network are state-of-the-art approaches in object detection. While Deformable Part Models makes use of the general structure between parts and root models, Convolutional Neural Network uses all information of input to create meaningful features. The...
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| 主要作者: | |
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| 格式: | Conference paper |
| 語言: | English |
| 出版: |
2019
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| 在線閱讀: | http://repository.vnu.edu.vn/handle/VNU_123/67098 |
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| 總結: | Deformable Part Models and Convolutional Neural Network are state-of-the-art approaches in object detection. While Deformable Part Models makes use of the general structure between parts and root models, Convolutional Neural Network uses all information of input to create meaningful features. These two types of characteristics are necessary for face detection. Inspired by this observation, first, we propose an extension of DPM by adaptively integrating CNN forface detection called DeepFace DPM and propose a new combined model for face representation. Second, a new way of calculating non-maximum suppression is also introduced to boost up detection accuracy. We use Face Detection Data Set and Benchmark to evaluate the merit of our method. 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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