Acta Optica Sinica, Volume. 39, Issue 11, 1115003(2019)
New Method for Face Landmark Detection Based on Stacked-Hourglass Network
Figures & Tables(13)
Fig. 4. Comparative experiment of face landmark detection on images with large posture changes and face partial occlusion
Fig. 5. CED of different methods for 300W competition dataset with inter-ocular normalization
Table 1. Error of face landmark detection methods on the 300W test set%
View tableTable 1. Error of face landmark detection methods on the 300W test set%
Condition Method Common subset Challenging subset Full set Inter- pupil normalization Method in Ref. [18] 6.65 19.79 9.22 Method in Ref. [19] 5.50 16.78 7.69 Method in Ref. [20] 5.28 17.00 7.58 Method in Ref. [4] 5.60 15.40 7.52 Method in Ref. [21] 5.25 13.62 6.40 Method in Ref. [22] 4.95 11.98 6.32 Method in Ref. [23] 4.51 13.80 6.31 Method in Ref. [24] 4.73 9.98 5.76 Method in Ref. [25] 4.80 8.60 5.54 Method in Ref. [26] 4.12 8.35 4.94 Method in Ref. [27] 3.67 7.62 4.44 FDL-PHR 3.22 7.92 4.14 Inter-ocular normalization Method in Ref. [27] 3.67 7.62 4.44 Method in Ref. [6] 3.33 6.99 4.05 Method in Ref. [28] 3.34 6.60 3.98 Method in Ref. [29] 3.34 6.56 3.97 FDL-PHR 3.11 5.71 3.62
Table 2.
N 0.08 and failure rate of face landmark detection methods on the 300W full test set by inter-ocular normalizationView tableTable 2.
N 0.08 and failure rate of face landmark detection methods on the 300W full test set by inter-ocular normalizationCondition Method N 0.08Failure /% Inter-ocular normalization Method in Ref. [4] 0.4294 10.89 Method in Ref. [20] 0.4312 10.45 Method in Ref. [24] 0.4987 5.08 Method in Ref. [6] 0.5212 4.21 FDL-PHR 0.6893 2.35
Table 3. Error of face landmark detection methods on face images with large posture changes and face partial occlusion by inter-ocular normalization
View tableTable 3. Error of face landmark detection methods on face images with large posture changes and face partial occlusion by inter-ocular normalization
Condition Method Error /% Inter-ocular normalization Method in Ref. [21] Method in Ref. [6] FDL-PHR 13.89 10.02 8.32
Table 4.
N 0.08and failure rate of facial landmark detection methods on the 300W competition dataset by inter-ocular normalizationView tableTable 4.
N 0.08and failure rate of facial landmark detection methods on the 300W competition dataset by inter-ocular normalizationCondition Method N 0.08Failure /% Inter-ocular normalization Method in Ref. [30] 0.1955 38.83 Method in Ref. [20] 0.3235 17.00 Method in Ref. [31] 0.3281 13.00 Method in Ref. [32] 0.3497 12.67 Method in Ref. [24] 0.3981 12.30 Method in Ref. [6] 0.4532 6.80 FDL-PHR 0.5805 2.86
Table 5. Error analysis of face landmark detection methods on the Menpo competition dataset by face diagonal normalization
View tableTable 5. Error analysis of face landmark detection methods on the Menpo competition dataset by face diagonal normalization
Condition Method Mean error Standard deviation Max error Face diagonal normalization Method in Ref. [33] 0.0205 0.0340 0.9467 Method in Ref. [34] 0.0182 0.0179 0.4661 Method in Ref. [35] 0.0165 0.0235 0.9612 Method in Ref. [36] 0.0159 0.0201 0.6717 Method in Ref. [37] 0.0200 0.0756 0.7290 Method in Ref. [38] 0.0135 0.0095 0.5098 Method in Ref. [29] 0.0138 0.0157 0.6312 Method in Ref. [39] 0.0139 0.0260 0.9624 Method in Ref. [9] 0.0120 0.0060 0.1453 FDL-PHR 0.0199 0.0071 0.07184
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Weichi Zhao, Qijie Zhao, Junye Jiang, Jianxia Lu. New Method for Face Landmark Detection Based on Stacked-Hourglass Network[J]. Acta Optica Sinica, 2019, 39(11): 1115003
Paper Information
Category: Machine Vision
Received: May. 7, 2019
Accepted: Jul. 15, 2019
Published Online: Nov. 6, 2019
The Author Email: Zhao Qijie (zqj@shu.edu.cn)
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