本文为美国密苏里大学（作者：MUHIND SALIM HMOUD ALRADAD）的硕士论文，共64页。
在图像分类方面，采用了基于卷积神经网络（CNN）的方法，并取得了目前最新的性能。人脸图像分析需要有效的特征提取和分类器系统。本研究以深度学习演算法为研究对象，探讨其在分类作业中的应用。展示了如何在CNN架构中选择平均值和最大池化。这种方法通过使用将平均池化和最大池化结合在一起的同类网络来改进效果。在基于人脸特征的性别分类中，CNN能够同时提取相关特征并对其进行分类。在两个无约束数据集上获得了最新的性能：标记的户外人脸（LFW）和人群图像数据集。利用CASPEAL-RL数据集对我们的系统进行了约束条件下的测试，所有的图像都是在实验室内采集的，在年龄估计方面，使用了一组人群的图像数据集，根据年龄将图像中的人分为七组，取得了很好的性能。
For image classification, I achieved thecurrent state-of-the-art performance by using methods based on ConvolutionalNeural Networks (CNNs). Face image analysis requires both effective featureextraction and classifier systems. This research considers the deep learningalgorithm and addresses its working for classification tasks. I shows how tochoose between averages and max pooling in CNN architecture. This methodimproved results by using homogeneous networks that combined average and maxpooling together. For gender classification based on facial features, CNNsproved effective for simultaneously extracting relevant features andclassifying them. State-of-the-art performance was obtained on twounconstrained datasets: Labeled Faces in the Wild (LFW) and Images of Groups ofpeople dataset. CASPEAL-RL dataset was used to test our systems underconstrained conditions where all the images were collected inside the lab. Forage estimation, I achieved good performance using images of groups of peopledataset where the people in the images have been divided into seven groupsaccording to their age.
- 引言
- 背景文献
- 性别与年龄分类
- 结论与未来工作展望
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