基于深度卷积和自编码器增强的微表情判别

付晓峰,牛力

Micro-expression classification based on deep convolution and auto-encoder enhancement

Xiao-feng FU,Li NIU

表 5 本文方法与现有方法的性能对比

Tab.5 Comparison of proposed method with existing methods

方法 联合数据库 SMIC CASME II SAMM UF1 UAR UF1 UAR UF1 UAR UF1 UAR LBP-TOP[18] 0.588 2 0.578 5 0.200 0 0.528 0 0.702 6 0.742 9 0.395 4 0.410 2 Bi-WOOF[19] 0.629 6 0.622 7 0.572 7 0.582 9 0.780 5 0.802 6 0.521 1 0.513 9 OFF-ApexNet[8] 0.719 6 0.709 6 0.681 7 0.669 5 0.876 4 0.868 1 0.540 9 0.539 2 CapsuleNet[20] 0.652 0 0.650 6 0.582 0 0.587 7 0.706 8 0.701 8 0.620 9 0.598 9 Dual-Inception[9] 0.732 2 0.727 8 0.664 5 0.672 6 0.862 1 0.856 0 0.586 8 0.566 3 STSTNet[21] 0.735 3 0.760 5 0.680 1 0.701 3 0.838 2 0.868 6 0.658 8 0.681 0 EMR[22] 0.788 5 0.782 4 0.746 1 0.753 0 0.829 3 0.820 9 0.775 4 0.715 2 LGCcon[5] — — — — 0.792 9 0.763 9 0.524 8 0.495 5 LGCconD[5] — — 0.619 5 0.606 6 0.776 2 0.749 9 0.492 4 0.471 1 MecNet 0.763 2 0.770 4 0.720 1 0.731 9 0.866 7 0.851 0 0.735 8 0.677 2 MegNet+MecNet 0.789 3 0.805 6 0.742 5 0.751 3 0.867 4 0.852 1 0.768 2 0.709 3