摘要翻译：
提出了一种鲁棒的彩色图像人脸检测和性别分类方法。以往关于性别识别的研究认为，为了对齐，对人脸图像进行预处理，使眼睛、鼻子、嘴唇、下巴等面部标志位于图像中的统一位置，需要进行昂贵的计算和耗时的预处理。本文提出了一种基于数学分析的新技术，分三个阶段消除了对准步骤。首先，提出了一种新的基于颜色的人脸检测方法，该方法在复杂背景下具有较好的检测效果和较强的鲁棒性。然后，利用尺度不变特征变换(SIFT)方法从每个人脸中提取出对仿射变换不变的特征。为了评价该算法的性能，在一个包含500幅男女比例相等的不同人的人脸图像数据库上使用支持向量机分类器进行了实验。
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英文标题：
《Gender Recognition Based on Sift Features》
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作者：
Sahar Yousefi, Morteza Zahedi
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  This paper proposes a robust approach for face detection and gender classification in color images. Previous researches about gender recognition suppose an expensive computational and time-consuming pre-processing step in order to alignment in which face images are aligned so that facial landmarks like eyes, nose, lips, chin are placed in uniform locations in image. In this paper, a novel technique based on mathematical analysis is represented in three stages that eliminates alignment step. First, a new color based face detection method is represented with a better result and more robustness in complex backgrounds. Next, the features which are invariant to affine transformations are extracted from each face using scale invariant feature transform (SIFT) method. To evaluate the performance of the proposed algorithm, experiments have been conducted by employing a SVM classifier on a database of face images which contains 500 images from distinct people with equal ratio of male and female.
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PDF链接：
https://arxiv.org/pdf/1108.1500