关键词: 眉毛识别, 图半监督学习, 支持向量机, 傅里叶变换, 主成分分析

Abstract: This paper proposes a semi-automatic eyebrow recognition method based on Hash Graph based Semi-supervised Learning（HGSL） and Support Vector Machines（SVMs）.HGSL is presented to tackle the problem of time-consuming graph construction in Graph based Semi-supervised Learning method（GSL），for segmenting and extracting pure eyebrow images more efficiently in a semi-automatic way.The extracted pure eyebrow images are translated into feature vectors by Fourier transform and Gabor transform as well as principal component analysis，which are applied to training SVMs for recognition.On the BJUT eyebrow database，a series of experiments have been performed to analyze the effect of GSL and HGSL on eyebrow segmentation speed，and to summarize the influence of them together with feature and kernel selection on recognition rate.

Key words: eyebrow recognition, graph based semi-supervised learning, support vector machines, Fourier transform, principal component analysis