Face Recognition Based on Wavelet Transform and Multifeature Fusion Coding

Fig. 4. Extraction effects of various algorithms before and after adding Gabor wavelet. (a) Raw face image; (b) LBP operator before adding Gabor wavelet; (c) AR-LGC operator before adding Gabor wavelet; (d) multi-feature fusion coding before adding Gabor wavelet; (e) LBP operator after adding Gabor wavelet; (f) AR-LGC operator after adding Gabor wavelet; (g) proposed method

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Fig. 5. Flow chart of face recognition

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Fig. 6. Recognition rate curve under different number of blocks

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Fig. 7. Treatment effect and process of proposed method

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Fig. 8. Part of images from different face databases. (a) Yale face database; (b) ORL face database

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Fig. 9. Part of images from different face databases. (a) FERET face database; (b) CMU-PIE face database

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Table 1. Feature dimension and average recognition rate under different number of blocks
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Table 1. Feature dimension and average recognition rate under different number of blocks
Number of blocks Feature dimension Average recognition rate /%
2×2 640 85.88
3×3 1440 88.80
4×4 2560 90.28
5×5 4000 95.03
9×9 12960 92.70
11×11 19360 89.30
16×16 40960 88.82

Table 2. Recognition rate of each algorithm in YALE face database unit: %

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Table 2. Recognition rate of each algorithm in YALE face database unit: %

Method	Recognition rate
Method	N=4	N=5	N=6	N=7	N=8
PCA^[10]	76.4	79.3	87.6	91.5	93.2
Gabor^[10]	84.1	90.0	92.0	92.3	94.1
HOG informationentropy weighting^[26]	85.3	89.1	93.1	94.5	97.8
Ref. [27]	92.0	92.2	93.5	94.8	96.3
LBP operator	84.5	88.9	93.8	94.5	94.9
AR-LGC operator	85.0	92.8	94.6	95.0	95.1
Proposed method	85.5	93.2	96.0	97.2	97.9

Table 3. Recognition rate of each algorithm in ORL face database unit: %

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Table 3. Recognition rate of each algorithm in ORL face database unit: %

Method	Recognition rate
Method	N=4	N=5	N=6	N=7	N=8
PCA^[10]	77.3	81.4	91.5	92.7	93.5
Gabor^[10]	84.5	89.1	91.7	93.1	94.0
HOG information entropy weighting^[26]	87.8	92.0	94.3	96.4	98.3
Ref. [27]	89.0	90.5	94.5	96.0	96.5
LBP operator	84.9	90.5	93.4	95.1	95.8
AR-LGC operator	85.2	92.6	94.5	95.8	96.0
Proposed method	85.4	93.9	96.1	97.6	98.5

Table 4. Recognition rate of each algorithm in FERET face database unit: %
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Table 4. Recognition rate of each algorithm in FERET face database unit: %
Method Recognition rate
be bj bf
LBP operator 65.8 68.1 65.5
AR-LGC operator 83.9 89.5 81.6
Proposed method 89.4 93.8 87.7

Table 5. Recognition rate of each algorithm in CMU-PIE face database unit: %

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Table 5. Recognition rate of each algorithm in CMU-PIE face database unit: %

Method	Recognition rate
Method	N=10	N=20	N=30	N=40	N=50
LBP operator^[25]	69.1	72.6	80.4	82.8	85.5
AR-LGC operator	71.0	75.2	85.1	86.0	88.4
Ref. [28]	73.8	83.4	86.2	--	--
Ref. [25]	74.8	84.7	87.6	--	--
Ref. [29]	78.6	87.0	90.0	90.2	90.3
Proposed method	75.8	80.6	90.2	91.3	94.6

Table 6. Average recognition time of each algorithm in CMU-PIE face database unit: ms
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Table 6. Average recognition time of each algorithm in CMU-PIE face database unit: ms
Method Average recognition time
LBP operator^[25] 22.36
AR-LGC operator 24.15
Ref. [28] 26.77
Ref. [25] 17.56
Ref. [29] 25.31
Proposed method 26.98

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Xiucai Guo, Haoran Cong. Face Recognition Based on Wavelet Transform and Multifeature Fusion Coding[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1210027

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