基于改进GD-HASLR算法的遮挡人脸识别

doi:10.16180/j.cnki.issn1007-7820.2023.06.011

Abstract

Abstract:

When the number of training samples is reduced, the recognition result of occlusion face recognition algorithm will also decrease. To solve this problem, an improved GD-HASLR algorithm is proposed. Firstly, the algorithm obtains the generalized gradient direction of the face image, and calculates the gradient size and gradient direction of the face image from the first order to the third order. Then, after mapping with the mapping function, the gradient direction vector is obtained, which is used as the input of the hierarchical sparse low-rank model, and the representation coefficient and error of the image are obtained. In this study, a restarted fast algorithm with shrinkage threshold-II is adopted to solve the sparse representation coefficient. Finally, the residuals of the first order to the third order test samples are calculated respectively, and the category with the highest frequency or the lowest average grade is selected as the classification result. Experimental results on AR and Extended Yale B databases show that compared with GD-HASLR and other methods, the recognition effect of the improved method proposed in this study is better.

Key words: occlusion, face recognition, generalized gradient direction, gradient size, gradient direction, hierarchical sparse low-rank model, restart fast iterative shrinkage thresholding algorithm-II, GD-HASLR

CLC Number:

TP391.4

XU Tiantian,XI Zhihong. Face Recognition with Occlusion Based on Improved GD-HASLR Algorithm[J].Electronic Science and Technology, 2023, 36(6): 72-79.

Figures/Tables 17

Figure 1.

Figure 2.

Figure 3.

算法1 重启FISTA-II的计算过程

步骤1 给定初始值l₀,L=L(f),c₁=1,令h₁=l₀;
步骤2 while(当条件不收敛时,进行第t次迭代)
1)计算l_t,l_t=

S ξ α

(h_t-2αD^T(Dh_t-b))。其中,α=1/L(f),L(f)=2λ_max(D^TD),λ_max(·)表示矩阵的最大特征值,ξ为分层模型的自适应权重函数;ξ=y-

e k + 1

+(1/β)z^k ;
2)更新c_t₊₁,c_t₊₁=(1+sqrt(1+4

c t 2

))/2
if(重启条件成立)
计算h_t₊₁,h_t₊₁=l_t
else
计算h_t₊₁,h_t₊₁=l_t+((c_t-1)/c_t₊₁)(l_t-l_t_-1)
end while

Figure 4.

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

Table 6.

Table 7.

Table 8.

Table 9.

Table 10.

Table 11.

Table 12.

References 20

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方法	NMR/%	GD-HASLR/%	改进的GD-HASLR/%
实验1	63.33	75.33	88.33
实验2	37.67	50.33	74.00
实验3	56.33	70.67	75.00
实验4	35.00	50.67	60.00
实验5	46.92	61.75	74.33
实验6	60.67	82.67	94.33
实验7	61.00	82.00	94.67
实验8	63.33	73.33	80.67
实验9	62.67	73.00	79.67
实验10	60.92	77.75	87.33

方法	GD-HASLR/%	改进的GD-HASLR/%
sigmoid	75.33	88.33
tan^-1(x)	74.67	88.00
softsign	74.00	88.00
tanh(x)	77.67	88.00

方法	GD-HASLR/%	改进的GD-HASLR
sigmoid	50.33	74.00
tan^-1(x)	53.00	73.00
softsign	53.00	73.67
tanh(x)	53.33	73.67

方法	GD-HASLR/%	改进的GD-HASLR/%
sigmoid	70.67	75.00
tan^-1(x)	71.67	73.67
softsign	70.67	73.33
tanh(x)	71.67	76.00

方法	GD-HASLR/%	改进的GD-HASLR/%
sigmoid	50.67	60.00
tan^-1(x)	51.00	53.33
softsign	48.67	53.33
tanh(x)	53.00	54.67

Face Recognition with Occlusion Based on Improved GD-HASLR Algorithm

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Figures/Tables 17

References 20

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方法	NMR/%	GD-HASLR/%	改进的GD-HASLR/%
实验1	51.32	75.88	69.74
实验2	66.23	85.09	94.74
实验3	75.00	86.40	94.30
实验4	14.91	53.07	58.77
实验5	8.33	72.81	77.63
实验6	28.95	64.47	78.07
实验7	33.55	64.47	65.57
实验8	36.62	78.95	86.18
实验9	51.75	75.44	86.18

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