Laser & Optoelectronics Progress, Volume. 56, Issue 21, 211501(2019)
Classroom Face Detection Algorithm Based on Convolutional Neural Network
This study proposes a face detection algorithm based on a convolutional neural network considering the scenario of a classroom, where the faces of students sitting in the back rows might not be visible. First, the algorithm extracts face features in two stages using a residual neural network. Then, it builds a feature pyramid and combines the Softmax loss function with center loss function to train a face recognition model based on a proper activation function. Upon applying the algorithm to the Wider Face dataset, it achieves an accuracy of 95.2% and mean average precision values of 93.0%, 87.3%, and 58.3% for three levels of validation sets, respectively.
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Meng Wang, Hansong Su, Gaohua Liu, Shen Li. Classroom Face Detection Algorithm Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(21): 211501
Category: Machine Vision
Received: Mar. 28, 2019
Accepted: Apr. 26, 2019
Published Online: Nov. 2, 2019
The Author Email: Liu Gaohua (suppig@126.com)