显著性引导的低光照人脸检测

Saliency guided low-light face detection

针对卷积神经网络难以对低光照环境拍摄的图像进行人脸检测的问题。提出了一种将图像显著性检测算法和深度学习相结合的算法,并应用于低光照人脸检测。所提算法将图像的显著性信息与图像原始RGB通道融合,用于神经网络训练。在低光照人脸数据集DARK FACE上进行了充分的实验,结果表明:所提方法在DARK FACE数据集上获得了比当前主流人脸检测算法更好的检测精度,进而验证了所提算法的有效性。

To deal with the problem that it is hard for convolution neural network to do face detection in low light environment,we propose a method combining image saliency and deep learning and apply it to low-light face detection,which integrates saliency and the original RGB channels of the image into neural network training.Sufficient experiments are implemented on DARK FACE,a low-light face dataset,and the results show that the proposed low-light face detection method achieves better detection accuracy than the existing mainstream face detection algorithms on DARK FACE,thus confirming the validity of the proposed method.