基于K均值聚类与肤色的人脸检测研究

Face detection based on K-means clustering and the skin-color

针对传统人脸检测中存在色偏所造成检测精度偏低,深度学习方法中通过训练大量数据来实现人脸检测而造成硬件要求高等问题。提出了训练简单的卷积神经网络来实现人脸和非人脸的判断,并利用白平衡算法来解决色偏的问题。将YCgCr颜色空间与K均值聚类的方法结合起来实现肤色检测,最后在肤色检测的基础上实现人脸检测。其精度相较于传统的人脸检测方法提升3%左右,速度比基于深度学习的人脸检测快2倍左右。

In view of the low detection accuracy caused by color bias in traditional face detection,the deep learning method realizes face detection by training a large amount of data,resulting in high hardware requirements.A simple convolutional neural network is proposed for face and non-face recognition,and then white balance algorithm is used to solve the problem of color cast.Skin-Color detection was realized by combining YCgCr color space with K-means clustering.Finally,face detection is realized on the basis of skin color detection.Its accuracy is about 3%higher than the traditional face detection method,and its speed is about twice faster than the face detection based on deep learning.