In order to increase accuracy in gender classification, an iterative learning approach combining support vector machine (SVM) and active appearance model (AAM) was proposed.

为了提高性别检测的精度，提出了一种支持向量机(SVM)与主动外观模型(aam)相结合的迭代学习算法。

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Active appearance model (AAM) is also studied for the localization of facial features. The parameters of texture model and appearance model are studied in detail in reconstruction of face images.

本文还研究了基于AAM的面部特征定位方法，分析了灰度模型参数及表观模型参数在人脸重构中的作用。

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