Face Recognition Algorithm Based on Attribute-Driven Loss Function

Fig. 2. Influences of super parameters λ and η on verification accuracy. (a) Verification accuracy with same η and different λ; (b) verification accuracy with different η and same λ

Download full size

Table 1. Comparison of decision boundaries of different loss functions in binary case
View table
Table 1. Comparison of decision boundaries of different loss functions in binary case
Loss function Decision boundary
Softmax loss (W₁-W₂)x+b₁-b₂=0
Modified Softmax loss ‖x‖cosθ₁-‖x‖cosθ₂=0
A-Softmax class 1: ‖x‖[cos(mθ₁)-cosθ₂]=0class 2: ‖x‖[cosθ₁-cos(mθ₂)]=0

Table 2. Comparison of verification accuracy of different network structures
View table
Table 2. Comparison of verification accuracy of different network structures
Network structure LFW /% CFP-FP /% AgeDB-30 /%
ResNet50 99.27 91.34 94.31
ResNet101 99.35 92.15 95.82
MobileNet 99.13 90.10 93.88
Inception-ResNet v2 99.67 93.00 97.42
DenseNet 99.54 92.39 96.40
SE-ResNet101 99.48 92.78 96.67

Table 3. Verification accuracy of different loss functions

View table

Table 3. Verification accuracy of different loss functions

Loss function type	LFW /%	CFP-FP /%	AgeDB-30 /%
Softmax	97.78	89.64	93.04
Triplet loss	98.65	90.22	95.88
Center loss	99.02	91.10	96.12
L-Softmax loss	99.15	91.90	96.20
A-Softmax loss	99.42	92.80	96.83
Modified A-Softmax loss	99.67	93.00	97.42

Table 4. Accuracy of different loss functions in MegaFace dataset

View table

Table 4. Accuracy of different loss functions in MegaFace dataset

Method	Protocol	Identificationaccuracy /%	Verificationaccuracy /%
Vocord-DeepVo1	Large	75.127	67.318
Google-FaceNet V8	Large	70.496	86.493
Softmax loss	Small	54.628	65.732
Triplet loss	Small	64.698	78.030
Center loss	Small	65.334	80.106
L-Softmax	Small	67.035	80.185
A-Softmax	Small	72.729	85.561
Modified A-Softmax loss	Small	74.531	87.134

Tools

Get Citation

Copy Citation Text

Shen Li, Hansong Su, Gaohua Liu, Huihua Wu, Meng Wang. Face Recognition Algorithm Based on Attribute-Driven Loss Function[J]. Laser & Optoelectronics Progress, 2019, 56(24): 241505

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites