Infrared and Laser Engineering, Volume. 51, Issue 12, 20220176(2022)
Rotating face detection based on convergent cascaded convolutional neural network
To solve the problem of low accuracy of multi-scale rotating face detection under complex conditions such as large-scale pose change and large-angle face rotation-in-plane, a rotating face detection method based on parallel cascade convolution neural network is proposed. Using a coarse-to-fine cascading strategy, multiple shallow convolutional neural networks are cascaded in parallel on multiple feature layers of the main network SSD. Face/non-face detection, face boundary box position update and face RIP angle estimation are gradually completed. Experimental results on Rotate FDDB dataset and Rotate Sub-WIDER FACE dataset show that the proposed method achieves advanced face detection. The detection precision of the method is 87.1% and the speed is 45 FPS when 100 false positives occur in the rotating Sub-WIDER FACE dataset, which proves that the method can achieve accurate rotating face detection with low time loss.
Get Citation
Copy Citation Text
Yue Qi, Yunyun Dong, Yiqin Wang. Rotating face detection based on convergent cascaded convolutional neural network[J]. Infrared and Laser Engineering, 2022, 51(12): 20220176
Category: Image processing
Received: Mar. 13, 2022
Accepted: May. 16, 2022
Published Online: Jan. 10, 2023
The Author Email: Wang Yiqin (applychance@126.com)