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A Face Detection Method Based on Cascade Convolutional Neural Network
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DOI：10.1007/s11042-018-6995-0.png)
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Abstract
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Cascade has been widely used in face detection where classifier with low computational cost can be firstly used to shrink most of the background while keeping the recall. In this paper, a new cascaded convolutional neural network method consisting of two main steps is proposed. During the first stage, low-pixel candidate window is used as an input such that the shallow convolutional neural network quickly extracts the candidate window. In the second stage, the window from the former stage is resized and used as an input to the corresponding network layer respectively. During the training period, joint online training is conducted for hard samples and the soft non-maximum suppression algorithm is used to test on the dataset. The whole network achieves improved performance on the FDDB and PASCAL face datasets.
Keywords:
Face detection
Cascade convolution structure
Soft non-maximum suppression
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