IEEE Trans Cybern. 2015 Nov;45(11):2535-45. doi: 10.1109/TCYB.2014.2376934. Epub 2014 Dec 10.
In this paper, we propose a new prototype-based discriminative feature learning (PDFL) method for kinship verification. Unlike most previous kinship verification methods which employ low-level hand-crafted descriptors such as local binary pattern and Gabor features for face representation, this paper aims to learn discriminative mid-level features to better characterize the kin relation of face images for kinship verification. To achieve this, we construct a set of face samples with unlabeled kin relation from the labeled face in the wild dataset as the reference set. Then, each sample in the training face kinship dataset is represented as a mid-level feature vector, where each entry is the corresponding decision value from one support vector machine hyperplane. Subsequently, we formulate an optimization function by minimizing the intraclass samples (with a kin relation) and maximizing the neighboring interclass samples (without a kin relation) with the mid-level features. To better use multiple low-level features for mid-level feature learning, we further propose a multiview PDFL method to learn multiple mid-level features to improve the verification performance. Experimental results on four publicly available kinship datasets show the superior performance of the proposed methods over both the state-of-the-art kinship verification methods and human ability in our kinship verification task.
在本文中，我们提出了一种新的基于原型的判别特征学习（PDFL）方法，用于亲属关系验证。与大多数先前的亲属关系验证方法不同，这些方法采用低级手工制作的描述符（如局部二值模式和 Gabor 特征）进行面部表示，本文旨在学习判别性的中级特征，以更好地描述面部图像的亲属关系，从而进行亲属关系验证。为此，我们从有标签的野外人脸数据集构建了一组具有未标记亲属关系的人脸样本作为参考集。然后，将训练人脸亲属关系数据集中的每个样本表示为中级特征向量，其中每个条目是来自一个支持向量机超平面的对应决策值。随后，我们通过最小化中级特征向量中的同类别样本（具有亲属关系）并最大化相邻异类样本（没有亲属关系）来制定优化函数。为了更好地利用多个低级特征进行中级特征学习，我们进一步提出了一种多视图 PDFL 方法，以学习多个中级特征来提高验证性能。在四个公开可用的亲属关系数据集上的实验结果表明，所提出的方法在我们的亲属关系验证任务中优于最先进的亲属关系验证方法和人类能力。
