Face Recognition Based on Improved Gradient Local Binary Pattern

Huixian Yang; Yong Chen; Fei Zhang; Tongtong Zhou

doi:10.3788/LOP55.061004

Journals >Laser & Optoelectronics Progress >Volume 55 >Issue 6 >Page 061004 > Article

Laser & Optoelectronics Progress
Vol. 55, Issue 6, 061004 (2018)

Face Recognition Based on Improved Gradient Local Binary Pattern

Huixian Yang^1、1;, Yong Chen^{、1*; *;}, Fei Zhang^1、1;, and Tongtong Zhou^2、2;

Author Affiliations

¹ School of Physics and Optoelectronics, Xiangtan University, Xiangtan, Hunan 411105, China

² College of Mechanical and Electrical Engineering, Hunan Applied Technology University,Changde, Hunan 415000, China

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DOI: 10.3788/LOP55.061004 Cite this Article Set citation alerts

Huixian Yang, Yong Chen, Fei Zhang, Tongtong Zhou. Face Recognition Based on Improved Gradient Local Binary Pattern[J]. Laser & Optoelectronics Progress, 2018, 55(6): 061004 Copy Citation Text

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Fig. 1. Sobel operator. (a) Horizontal direction; (b) vertical direction; (c) 45° direction; (d) 135° direction

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Fig. 2. (a) Sampling point; (b) LBP encoding; (c) GLBP encoding

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Fig. 3. Robustness of GLBP versus LBP. (a) Original encoding; (b) noise encoding

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Fig. 4. (a) Traditional feature extraction; (b) improved feature extraction

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Fig. 5. IGLBP encoding process

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Fig. 6. Flow chart of the proposed algorithm

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Fig. 7. Flow chart of IGLBP feature extraction

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Fig. 8. (a) Training sample; (b) AR expression subset; (c) AR illumination subset; (d) AR partial occlusion subset A; (e) AR partial occlusion subset B

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Fig. 9. (a) Training sample; (b) CAS-PEAL expression subset; (c) CAS-PEAL background subset; (d) CAS-PEAL accessory subset

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Fig. 10. YALE face library

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Algorithm	Facialexpressionsubset	Illuminationsubset	Partialocclusionsubset A	Partialocclusionsubset B
LBP	93.33	92.33	91.67	72.67
LDP	96.33	93.00	90.00	71.67
CSLBP	96.67	95.00	87.67	73.00
LGBP	94.67	99.00	94.33	90.33
IGLBP	99.67	99.00	98.67	94.33

Table 1. Recognition rate on AR database of different algorithms%

Algorithm	Backgroundsubset	Expressionsubset	Accessorysubset
LBP	91.00	89.00	91.00
LDP	97.33	95.00	86.00
CSLBP	93.25	87.25	90.25
LGBP	97.52	91.25	93.00
IGLBP	99.75	96.25	97.00

Table 2. Recognition rate on CAS-PEAL database of different algorithms%

Algorithm	Number of sample
Algorithm	2	3	4	5
LBP	73.25	77.66	78.00	78.33
LDP	78.00	81.75	82.34	84.75
CSLBP	79.37	82.06	83.00	85.72
LGBP	85.32	90.09	92.33	93.25
IGLBP	86.61	92.05	93.57	94.53

Table 3. Recognition rate on YALE database of different algorithms%

Algorithm	σ=0	σ=0.0001	σ=0.0002	σ=0.0003	σ=0.0004	δ
LBP	91.00	36.25	16.00	9.25	6.75	92.58
LDP	97.33	95.00	93.25	90.50	87.75	9.84
CSLBP	93.25	90.25	83.52	76.25	65.25	30.03
LGBP	97.52	96.25	95.75	94.00	93.25	4.38
IGLBP	99.75	99.50	99.25	99.00	98.50	1.25

Table 4. Results on CAS-PEAL background subset of different algorithms after adding different variances of Gaussian noise%

Algorithm	σ=0	σ=0.0001	σ=0.0002	σ=0.0003	σ=0.0004	δ
LBP	89.00	28.50	13.50	7.25	6.25	92.98
LDP	95.00	91.25	87.25	82.98	78.00	17.89
CSLBP	87.25	83.50	79.25	74.75	63.25	27.51
LGBP	91.25	90.50	89.00	88.50	86.75	4.93
IGLBP	96.25	92.50	92.00	91.25	90.75	5.71

Table 5. Results on CAS-PEAL expression subset of different algorithms after adding different variances of Gaussian noise%

Algorithm	σ=0	σ=0.0001	σ=0.0002	σ=0.0003	σ=0.0004	δ
LBP	91.00	26.25	14.00	8.50	6.50	92.86
LDP	86.00	83.50	79.25	74.75	71.67	16.66
CSLBP	90.25	97.25	81.00	74.75	63.00	30.19
LGBP	93.00	91.25	90.50	89.75	87.65	5.75
IGLBP	97.00	94.75	94.50	93.50	92.75	4.90

Table 6. Results on CAS-PEAL accessory subset of different algorithms after adding different variances of Gaussian noise%

Algorithm	Feature dimension	T₁ /ms	T₂ /ms
LBP	16384	30.3	23.2
LDP	16384	439.1	22.9
CSLBP	1024	23.6	2.5
LGBP	655360	883.5	102.6
IGLBP	16384	94.5	23.0

Table 7. Feature dimensions and average time of different algorithms on YALE database

Huixian Yang, Yong Chen, Fei Zhang, Tongtong Zhou. Face Recognition Based on Improved Gradient Local Binary Pattern[J]. Laser & Optoelectronics Progress, 2018, 55(6): 061004

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