feature extraction and fusion using deep convolutional neural networks for face detection

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ID: 196723
2017
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Abstract
This paper proposes a method that uses feature fusion to represent images better for face detection after feature extraction by deep convolutional neural network (DCNN). First, with Clarifai net and VGG Net-D (16 layers), we learn features from data, respectively; then we fuse features extracted from the two nets. To obtain more compact feature representation and mitigate computation complexity, we reduce the dimension of the fused features by PCA. Finally, we conduct face classification by SVM classifier for binary classification. In particular, we exploit offset max-pooling to extract features with sliding window densely, which leads to better matches of faces and detection windows; thus the detection result is more accurate. Experimental results show that our method can detect faces with severe occlusion and large variations in pose and scale. In particular, our method achieves 89.24% recall rate on FDDB and 97.19% average precision on AFW.
Reference Key
lu2017mathematicalfeature Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Xiaojun Lu;Xu Duan;Xiuping Mao;Yuanyuan Li;Xiangde Zhang
Journal journal of power sources
Year 2017
DOI
10.1155/2017/1376726
URL
http://dx.doi.org/10.1155/2017/1376726
Keywords
civil engineering (general)mathematics engineering (general)

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