论文笔记2.3——PFLD: A Practical Facial Landmark Detector

3，Experimental Evaluation

3.1 实验设置

Datasets. To evaluate the performance of our proposed

PFLD, we conduct experiments on two widely-adopted

challenging datasets, say 300W [ 25 ] and AFLW [ 18 ].

300W. This dataset annotates fifive face datasets including

LFPW, AFW, HELEN, XM2VTS and IBUG, with 68 land

marks. We follow [ 9 , 34 , 19 ] to utilize 3,148 images for

training and 689 images for testing. The testing images are

divided into two subsets, say the common subset formed by

554 images from LFPW and HELEN, and the challenging

subset by 135 images from IBUG. The common and the

challenging subsets form the full testing set.

AFLW . This dataset consists of 24,386 in-the-wild human

faces, which are obtained from Flicker with extreme poses,

expressions and occlusions. The faces are with head pose

ranging from 0 ◦ to 120 ◦ for yaw, and upto 90 ◦ for pitch

and roll, respectively. AFLW offers at most 21 landmarks

for each face. We use 20,000 images and 4,386 images for

training and testing, respectively

 

数据集：

为了评价我们提出的PFLD的表现效果，我们在两个被广泛接受的具有挑战性的数据集即 300W【25】和 AFLW【18】。这个数据集标注出了五个人脸数据集，包括 LFPW ,AFW, HELEN, XM2VTS 和 IBUG 有68个标记。我们的实验像【9，34，19】一样使用3148张图用来训练，689张图用来测试。测试用图被分成两组，有554张来自LFPW和HELEN的554张图，剩下135张图具有挑战性的，来自IBUG。

AFLW 这个数据集有24386张自然条件下的人脸图片，是从闪拍器中获得，包括很多极端的姿势，表情和遮挡情况。这些人连的头部倾斜角度从0°到120° for yaw ，到90° for pitch and roll ，AFLW提供最多每张脸21个关键点位置。使用2w和4386张图分别用来训练和测试。

竞争者：

The compared approaches include classic

and recently proposed deep learning based schemes,

which are RCPR (ICCV’13) [ 4 ], SDM (CVPR’13)

[ 38 ], CFAN (ECCV’14) [ 42 ], CCNF (ECCV’14) [ 1 ], ESR

(IJCV’14) [ 5 ], ERT (CVPR’14) [ 16 ], LBF (CVPR’14) [ 24 ],

TCDCN (ECCV’14) [ 45 ], CFSS (CVPR’15) [ 46 ], 3DDFA

(CVPR’16) [ 48 ], MDM (CVPR’16) [ 29 ], RAR (ECCV’16)

[ 37 ], CPM (CVPR’16) [ 33 ], DVLN (CVPRW’17) [ 35 ],

TSR (CVPR’17) [ 22 ], Binary-CNN (ICCV’17) [ 3 ],

PIFA-CNN (ICCV’17) [ 15 ], RDR (CVPR’17) [ 36 ],

DCFE (ECCV’18) [ 30 ], SeqMT (CVPR’18) [ 12 ], PCD

CNN (CVPR’18) [ 19 ], SAN (CVPR’18) [ 9 ] and LAB

(CVPR’18) [ 34 ].

 

评价优点：

和之前的工作类似【5，34，9，19】，使用归一化的平均误差来作为测量精度的标准，从所有的关键点标记中计算平均值。对于300W，我们用两个标准化的因素来报告结果，一个采用 eye-center-distance 作为瞳间距离的标准化因素。对于ALFW来说，因为侧脸变化多端，我们像【19，9，34】一样归一化 由真实边界框大小得到的误差。 积累误差分布 （CED）曲线也被用来比较这些方法。除了精度之外，处理速度和模型的体量也是被比较的重要因素。

 3.2 实验结果

检测精度。

模型体量。

处理速度比较。

消融实验。

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