A Face Recognition Algorithm Based on Adaptive Weighted Curvelet Gradient Direction Histogram

Huixian Yang; Xiaoxiao Li; Weifa Gan

doi:10.3788/LOP57.101011

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 10 >Page 101011 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 10, 101011 (2020)

A Face Recognition Algorithm Based on Adaptive Weighted Curvelet Gradient Direction Histogram

Huixian Yang, Xiaoxiao Li^*, and Weifa Gan

Author Affiliations

Physics and Optoelectronic Engineering College, Xiangtan University, Xiangtan, Hunan 411105, China

show less

DOI: 10.3788/LOP57.101011 Cite this Article Set citation alerts

Huixian Yang, Xiaoxiao Li, Weifa Gan. A Face Recognition Algorithm Based on Adaptive Weighted Curvelet Gradient Direction Histogram[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101011 Copy Citation Text

show less

Fig. 1. Fusion images at four scales. (a) Scale 1; (b) scale 2; (c) scale 3; (d) scale 4

Download full size

Fig. 2. Flow of AWCHOG algorithm

Download full size

Fig. 3. ORL face dataset. (a) Image 1; (b) image 2; (c) image 3; (d) image 4; (e) image 5; (f) image 6

Download full size

Fig. 4. AR face dataset. (a) Training sample; (b) facial express subset; (c) illumination subset; (d) partial occlusion subset A; (e) partial occlusion subset B

Download full size

Fig. 5. CAS-PEAL face dataset. (a) Training sample; (b) Express subset; (c) Background subset; (d) Accessory subset

Download full size

Fig. 6. Recognition rate of ORL face dataset at different gradient directions

Download full size

Fig. 7. Recognition rate of ORL face dataset in different block modes. (a) Coarse floor; (b) detail 1 floor; (c) detail 2 floor; (d) fine floor

Download full size

Coefficient level	Coefficient matrix form	Dimension
Coarse	19×15 matrix	285
Detail 1	1×8 cell consisting of the matrix of size 17×31 or 37×15	4328
Detail 2	1×16 cell consisting of the matrix of size 32×31 or 28×30 or 38×26 or 38×23	14776
Fine	112×92 matrix	10304

Table 1. Form of Curvelet coefficients after Curvelet transform for 112 pixel×92 pixel face image

Scale	Recognition rate /%
Scale	ORL	AR	CAS-PEAL
1	90.00	91.00	98.14
2	92.50	92.33	98.54
3	93.00	94.67	98.89
4	95.50	95.67	99.20
5	89.50	90.67	95.94

Table 2. Recognition rate of Curvelet transform at different scales

Algorithm	Recognition rate /%
Algorithm	Image 2	Image 3	Image 4	Image 5	Image 6
Wavelet	83.13	88.21	87.92	93.74	96.50
HOG	85.00	89.29	92.50	97.00	96.88
Curvelet+PCA+SRC	88.21	93.33	94.00	95.00	95.83
Gabor+HOG	90.41	94.61	97.21	98.20	98.69
NSCT+LBP	89.14	94.79	96.20	98.11	98.69
AWNHOG	82.25	87.21	91.25	95.00	96.88
AWCHOG+CRC	90.12	86.79	92.50	96.50	97.33
AWCHOG+KNN	90.71	95.50	96.88	98.75	99.27

Table 3. Recognition rate on ORL database

Algorithm	Recognition rate /%
Algorithm	Expression subset	Accessory subset	Background subset
Wavelet	96.45	78.61	78.89
HOG	99.20	69.00	93.00
Curvelet+PCA+SRC	97.34	78.89	80.83
Gabor+HOG	99.73	87.22	95.56
NSCT+LBP	99.38	74.25	92.75
AWNHOG	99.63	80.50	99.00
AWCHOG+CRC	99.33	98.67	95.00
AWCHOG+KNN	99.75	97.50	98.50

Table 4. Recognition rate on CAS-PEAL database

Algorithm	Recognition rate /%
Algorithm	Facial expresssubset	Illuminationsubset	Partial occlusionsubset A	Partial occlusionsubset B
Wavelet	92.67	80.50	79.50	62.00
HOG	91.33	90.33	69.00	49.00
Curvelet+PCA+SRC	94.67	85.50	91.33	73.67
Gabor+HOG	96.33	97.50	81.00	76.50
NSCT+LBP	96.67	98.33	96.67	76.00
AWNHOG	98.33	99.67	96.67	82.67
AWCHOG+CRC	95.33	96.67	93.67	80.33
AWCHOG+KNN	99.50	98.89	96.00	90.00

Table 5. Recognition rate on AR database

Method	Normalized variance of Gaussion white noise						φ
Method	0		0.0001		0.0002	0.0003	0.0004
Wavelet	80.50	39.00		21.50	16.67	14.67	81.78
HOG	90.33	68.33		58.67	53.67	52.33	42.06
Curvelet+PCA+SRC	85.50	71.67		68.33	67.67	65.67	23.19
Gabor+HOG	97.50	86.00		84.67	81.00	79.33	18.63
NSCT+LBP	98.33	91.50		85.50	74.00	70.50	28.30
AWNHOG	99.67	93.67		90.33	83.67	79.67	20.06
AWCHOG+KNN	98.89	98.00		96.67	91.67	90.67	8.31

Table 6. Results of different algorithms on AR illumination subset after adding Gaussian noise

Method	Featuredimensionality	T₁ /ms	T₂ /ms
Wavelet	1920	16.4	14.2
HOG	320	4.5	38.1
Curvelet+PCA+SRC	2850	33.2	41.4
Gabor+HOG	9600	163.3	98.9
NSCT+LBP	3696	856.3	30.1
AWNHOG	2048	15.9	38.3
AWCHOG+KNN	2272	14.5	35.7

Table 7. Dimensionality and time of different algorithms on ORL face dataset

Huixian Yang, Xiaoxiao Li, Weifa Gan. A Face Recognition Algorithm Based on Adaptive Weighted Curvelet Gradient Direction Histogram[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101011

Download Citation

Set citation alerts for the article

Tools

Set citation alerts for the article

Save the article for my favorites

Paper Information