复杂光照场景下基于MTCNN的人脸检测

为了提高复杂光照条件下的人脸检测识别率,提出了一种基于Retinex图像增强技术应用于多任务卷积神经网络(multi-task cascaded convolutional networks,MTCNN)的人脸测算法.算法用Retinex理论对图像进行增强,能明显提高MTCNN在不同光照场景下的人脸检测精度,同时使面部...

Face Detection Based on MTCNN in Complex Lighting Scenes

In order to improve the recognition rate of face detection under complex lighting conditions, a face detection algorithm based on Retinex image enhancement technology applied to multi-task cascaded convolutional networks (MTCNN) was proposed. The algorithm uses Retinex theory to enhance the image, which can significantly improve the face detection accuracy of MTCNN in different lighting scenarios, and make the positioning of the five key points of the face more accurate at the same time. Experiments have proved that this method has better results than the original MTCNN network in face detection in complex lighting scenes, and is beneficial to the later face alignment and classification tasks.