基于多分辨率LPQ和SIFT的无约束性别分类

Huu-Tuan Nguyen, T. Huong
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引用次数: 3

摘要

建立性别分类最关键的任务之一是如何将人脸描述为高度判别的特征向量。为此,本文引入了一种新的手工特征提取方法来解决无约束性别分类问题。从一个输入的人脸图像中,我们生成其较小的版本,并在它们上应用两个LPQ算子。然后,我们将得到的LPQ特征与从给定图像中提取的SIFT特征结合起来,构成一个全局的面部描述。在分类阶段,使用二值支持向量机分类器确定测试图像的性别。为了评估所提方法的识别性能,我们在两个广泛使用的无约束人脸数据库Adience和LFW上进行了实验。结果表明,我们的方法获得了良好的分类率(在LFW和Adience数据库上分别为96.51%和80.5%),可以与最先进的系统相媲美。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unconstrained gender classification by multi-resolution LPQ and SIFT
One of the most critical tasks in building a gender classification is how to describe the human face as a highly discriminative feature vector. To this end, in this paper we introduce a new handcrafted feature extraction method for unconstrained gender classification problem. From one input face image, we generate its smaller version and apply two LPQ operators on both of them. We then combine the obtained LPQ features with the SIFT feature extracted from the given image to constitute a global facial description. In the classification stage, the binary SVM classifier is used for determining the gender of the test images. To evaluate the recognition performance of the proposed methods, we carry out experiments upon two widely used unconstrained face databases Adience and LFW. The results show that our approach attains good classification rates (96.51% and 80.5% on LFW and Adience databases, respectively) and can be comparable with state-of-the-art systems.
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