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Gender classification of full body images based on the convolutional neural network

机译：基于卷积神经网络的全身图像性别分类

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摘要

Gender classification is one of the most interesting and challenging problems in computer vision and has been widely studied based on facial images. However, the images of human we taken from the real-world surveillance are mostly full body and relatively blurry, which is much more difficult to classify due to different poses and backgrounds in unconstrained scenarios. In this paper, we propose a new network structure based on a convolutional neural network (CNN), which is less complicated and has a small number of layers. Moreover, it can achieve a high accuracy with even trained with limited data. We evaluate our method on the dataset collected from real-world video surveillance and compare various learning algorithms including Alex Net and Google Net. The experimental results showed that the proposed model achieved better results than the tested state-of-the-art network structures.
机译：性别分类是计算机视觉中最有趣和最具挑战性的问题之一，并且已经基于面部图像进行了广泛的研究。但是，我们从真实世界的监视中拍摄的人类图像大多是全身的，并且相对模糊，由于在不受限制的场景中使用不同的姿势和背景，因此很难分类。在本文中，我们提出了一种基于卷积神经网络（CNN）的新网络结构，该网络结构不那么复杂并且层数少。此外，即使使用有限的数据进行训练，也可以实现高精度。我们根据从真实视频监控收集的数据集评估我们的方法，并比较包括Alex Net和Google Net在内的各种学习算法。实验结果表明，与经过测试的最新网络结构相比，所提出的模型取得了更好的结果。

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