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首页> 外文期刊>Computer vision and image understanding >Facial Feature Extraction Using Complex Dual-tree Wavelet Transform
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Facial Feature Extraction Using Complex Dual-tree Wavelet Transform

机译：复杂双树小波变换的人脸特征提取

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In this paper, we propose a novel method for facial feature extraction using the directional multiresolution decomposition offered by the complex wavelet transform. The dual-tree implementation of complex wavelet transform offered by Selesnick is used (DT-DWT(S)) [I.W., Selesnick, R.G. Baraniuk, N.C. Kingsbury, The dual-tree complex wavelet transform, IEEE Signal Processing Magazine, 6, s.l., IEEE, November 2005, vol. 22, pp. 123-151.]. In the dual-tree implementation, two parallel discrete wavelet transform (DWT) with different lowpass and highpass filters in different scales are used. The linear combination of subbands generated by two parallel DWT is used to generate 6 different directional subbands with complex coefficients. A test statistic, which is derived with absolute value of complex coefficient, whose distribution matches very closely with the directional information in the 6 subbands of the DT-DWT(S) is derived and used for detecting facial feature edges. The use of the complex wavelet transform is motivated by the fact that it helps eliminate the effects of non-uniform illumination, and the directional information provided by the different subbands makes it possible to detect edge features with different directionalities in the corresponding image. Edge information of facial area is enhanced using multiresolution structure of DT-DWT(S). The proposed method also employs an adaptive skin colour model instead of a predefined skin colour statistic. The model is developed with a unimodal Gaussian distribution using the skin region which is extracted excluding the detected edge map obtained from the DT-DWT(S). By combining the edge information obtained by using DT-DWT(S) and the non-skin areas obtained from the pixel statistics, the facial features are extracted. The algorithm is tested over the well known Carnegie Mellon University (CMU) and Marks Weber face databases. The average detection rate of the proposed method using DT-DWT(S) provides up to 9.6% improvement over the same method using discrete wavelet transform (DWT).
机译：在本文中，我们提出了一种使用复杂小波变换提供的方向性多分辨率分解的面部特征提取新方法。使用了由Selesnick提供的复数小波变换的双树实现（DT-DWT（S））[I.W.，Selesnick，R.G。 Baraniuk，N.C. Kingsbury，《双树复数小波变换》，《 IEEE信号处理杂志》，第6卷，第1版，IEEE，2005年11月，第1卷。 22，第123-151页]。在双树实现中，使用两个并行的离散小波变换（DWT），它们具有不同比例的不同低通和高通滤波器。由两个并行DWT生成的子带的线性组合用于生成6个具有复系数的不同方向性子带。导出由复数系数的绝对值得出的测试统计量，其分布与DT-DWT（S）的6个子带中的方向信息非常接近，并用于检测面部特征边缘。复数小波变换的使用是因为它有助于消除不均匀照明的影响，并且不同子带提供的方向信息可以检测相应图像中具有不同方向的边缘特征。使用DT-DWT（S）的多分辨率结构增强了面部区域的边缘信息。所提出的方法还采用自适应肤色模型而不是预定的肤色统计。该模型使用皮肤区域的单峰高斯分布进行开发，该皮肤区域的提取不包括从DT-DWT获得的检测到的边缘图。通过组合使用DT-DWT（S）获得的边缘信息和从像素统计信息获得的非皮肤区域，提取出面部特征。该算法在著名的卡内基梅隆大学（CMU）和Marks Weber人脸数据库上进行了测试。与使用离散小波变换（DWT）的相同方法相比，使用DT-DWT（S）的方法的平均检测率提高了9.6％。

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