基于K均值聚类与肤色的人脸检测研究

Translated title of the contribution: Face detection based on K-means clustering and the skin-color

Shen Zhang, Qingmei Huang*, Lijia Sun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Translated title of the contributionFace detection based on K-means clustering and the skin-color
Original languageChinese (Traditional)
Pages (from-to)301-306
Number of pages6
JournalGuangxue Jishu/Optical Technique
Volume48
Issue number3
Publication statusPublished - May 2022

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