Cascaded face alignment via intimacy definition feature
Cascaded face alignment via intimacy definition feature
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DOI：
10.1117/1.jei.26.5.053024
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发表时间：
2016-11
影响因子：
1.1
通讯作者：
Hailiang Li;K. Lam;Edmond M. Y. Chiu;Kangheng Wu;Zhibin Lei
中科院分区：
文献类型：
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作者：
Hailiang Li;K. Lam;Edmond M. Y. Chiu;Kangheng Wu;Zhibin Lei
抽象的。近年来，基于回归的面部对齐器逐渐流行，它直接学习面部外观和形状增量流形之间的映射。我们提出了一种基于随机森林的级联回归模型，通过使用局部轻量级特征（即亲密定义特征）来进行人脸对齐。该特征比姿态索引特征更具辨别力，比定向梯度直方图特征和尺度不变特征变换特征更有效，并且比局部二值特征（LBF）更紧凑。我们的算法的实验验证表明，我们的方法在一些具有挑战性的数据集上进行测试时实现了最先进的性能。与基于LBF的算法相比，我们的方法实现了大约两倍的速度，对齐精度提高了20%，并且节省了一个数量级的内存需求。
Abstract. Recent years have witnessed the emerging popularity of regression-based face aligners, which directly learn mappings between facial appearance and shape-increment manifolds. We propose a random-forest based, cascaded regression model for face alignment by using a locally lightweight feature, namely intimacy definition feature. This feature is more discriminative than the pose-indexed feature, more efficient than the histogram of oriented gradients feature and the scale-invariant feature transform feature, and more compact than the local binary feature (LBF). Experimental validation of our algorithm shows that our approach achieves state-of-the-art performance when testing on some challenging datasets. Compared with the LBF-based algorithm, our method achieves about twice the speed, 20% improvement in terms of alignment accuracy and saves an order of magnitude on memory requirement.