Gender classification and face recognition both need to find effective and stable facial features, but ultimately identify the different patterns.
性别识别和人脸识别一样,都需要寻找有效而又稳定的面部特征,只是最终识别类别不同。
参考来源 - 基于人脸图像的性别识别与年龄估计研究·2,447,543篇论文数据，部分数据来源于NoteExpress
Gender classification is one of the most biologically important and probably the easiest and fastest to achieve by people.
性别是人类重要的生物特征，人类可以很快的对其进行辨识。
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。
Despite the classification, there are more complex factors such as relational closeness, gender, age, and context that can affect how someone views physical contact.
尽管进行了分类，但还有一些更复杂的因素，如关系亲密度、性别、年龄和背景，可能会影响人们对身体接触的看法。
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