IEEE Trans Image Process. 2015 Dec;24(12):5356-68. doi: 10.1109/TIP.2015.2481327. Epub 2015 Sep 23.
In this paper, we propose a cost-sensitive local binary feature learning (CS-LBFL) method for facial age estimation. Unlike the conventional facial age estimation methods that employ hand-crafted descriptors or holistically learned descriptors for feature representation, our CS-LBFL method learns discriminative local features directly from raw pixels for face representation. Motivated by the fact that facial age estimation is a cost-sensitive computer vision problem and local binary features are more robust to illumination and expression variations than holistic features, we learn a series of hashing functions to project raw pixel values extracted from face patches into low-dimensional binary codes, where binary codes with similar chronological ages are projected as close as possible, and those with dissimilar chronological ages are projected as far as possible. Then, we pool and encode these local binary codes within each face image as a real-valued histogram feature for face representation. Moreover, we propose a cost-sensitive local binary multi-feature learning method to jointly learn multiple sets of hashing functions using face patches extracted from different scales to exploit complementary information. Our methods achieve competitive performance on four widely used face aging data sets.
本文提出了一种基于代价敏感局部二值特征学习（CS-LBFL）的人脸年龄估计方法。与传统的人脸年龄估计方法采用手工制作的描述符或整体学习的描述符进行特征表示不同，我们的 CS-LBFL 方法直接从原始像素学习判别性的局部特征进行人脸表示。受人脸年龄估计是一个代价敏感的计算机视觉问题的启发，以及局部二值特征比整体特征更能抵抗光照和表情变化的影响，我们学习了一系列哈希函数，将从人脸斑块中提取的原始像素值投影到低维二进制代码中，其中具有相似年龄的二进制代码尽可能接近地投影，而具有不同年龄的二进制代码尽可能远地投影。然后，我们在每个人脸图像中对这些局部二进制代码进行汇集和编码，作为人脸表示的实值直方图特征。此外，我们提出了一种代价敏感的局部二进制多特征学习方法，使用从不同尺度提取的人脸斑块共同学习多组哈希函数，以利用互补信息。我们的方法在四个广泛使用的人脸老化数据集上取得了有竞争力的性能。
