[1]WANG Chengji,LUO Zhiming,ZHONG Zhun,et al.Face detection method fusing multi-layer features[J].CAAI Transactions on Intelligent Systems,2018,13(1):138-146.[doi:10.11992/tis.201707018]
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Face detection method fusing multi-layer features
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
13
Number of periods:
2018 1
Page number:
138-146
Column:
学术论文—机器感知与模式识别
Public date:
2018-01-24
- Title:
- Face detection method fusing multi-layer features
- Keywords:
- face detection; multi pose; multi scale; occlude; complex scenes; convolutional neural network; feature fusion; non-maximum suppression
- CLC:
- TP391.41
- DOI:
- 10.11992/tis.201707018
- Abstract:
- To address the issues of pose, lighting variation, and scales, convolutional neural networks (CNNs) need to learn features with strong discrimination handle the face detection problem in complex scenes. Owing to the size limitations of the specific feature layer’s receptive field in convolutional neural networks, the features computed from a single layer of the CNNs are incapable of dealing with faces in multi poses and multi scales. Therefore, a multi-layer feature fusion method that is realized by fusing the different sizes of receptive fields is proposed to detect diversified faces. Moreover, via introducing the method of weighted score decrease, the present usual non-maximum suppression algorithm was improved to deal with the detection omission of neighboring faces caused by shielding. The experiment results with the FDDB and WiderFace datasets demonstrated that the fusion method proposed in this study can significantly boost detection performance, while the improved non-maximum suppression algorithm can increase the detection accuracy between neighboring faces.
- References:
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Last Update:
2018-02-01