Face Feature Extraction Using Elliptical Model Based Background Deletion and Generalized FEM

Yun-Su Chung, Sung-Uk Jung, Younglae Bae, Kiyoung Moon
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引用次数: 1

Abstract

This paper addresses a new face feature extraction method using elliptical model based background deletion and the generalized facial energy map (FEM). First of all, the method utilizes the elliptical model of a face to get a good normalized face image. This elliptical model based approach, thus, can easily delete the background region of high complexity. Next, the method generates a generalized FEM from the transformed data set of normalized face images. Finally, the coefficients of DCT, potentially containing important meanings, are extracted with the above generalized FEM, and are analyzed by using LDA. Experimental results show that the method effectively extracts feature vectors with reasonable time complexity, and has good recognition performance.
基于背景删除和广义FEM的椭圆模型人脸特征提取
提出了一种基于椭圆模型的背景删除和广义面能图的人脸特征提取新方法。首先,该方法利用人脸的椭圆模型得到良好的归一化人脸图像。因此,这种基于椭圆模型的方法可以很容易地删除高复杂度的背景区域。然后,该方法从转换后的归一化人脸图像数据集生成广义有限元。最后,利用上述广义有限元法提取可能包含重要意义的离散余弦变换系数,并利用LDA进行分析。实验结果表明,该方法能有效提取具有合理时间复杂度的特征向量,具有良好的识别性能。
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