Identity Authentication for Smart Phones Based on an Optimized Convolutional Deep Belief Network

Table 1. Simulation results of different network depths
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Table 1. Simulation results of different network depths
Depth ACC /% FAR /% FRR /% Time /s
1 93.524 6.22 8.00 60.90
2 97.667 2.22 3.33 130.56
3 92.333 7.11 10.67 251.12

Table 2. Simulation results of different sparsity indices
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Table 2. Simulation results of different sparsity indices
Index ACC /% FAR /% FRR /% Time /s
0.010 96.333 2.80 8.00 109.36
0.015 97.000 2.00 8.00 115.98
0.020 97.667 2.22 3.33 130.56
0.025 95.000 0 30.00 134.02
0.200 83.333 0 100.00 139.88

Table 3. Simulation results of different pooling methods
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Table 3. Simulation results of different pooling methods
Method ACC /% FAR /% FRR /% Time /s
None 97.333 2.00 6.00 400.31
Mean 97.652 2.33 3.35 130.77
Max 97.667 2.22 3.33 130.56

Table 4. Simulation results of different iteration times
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Table 4. Simulation results of different iteration times
Epoch ACC /% FAR /% FRR /% Time /s
5^∈ 96.000 0.80 20.00 70.33
10^∈ 97.667 2.22 3.33 130.56
25^∈ 96.000 2.80 10.00 375.61
50^∈ 97.000 2.00 8.00 643.80

Table 5. Simulation results of different connection layers
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Table 5. Simulation results of different connection layers
Layer ACC /% FAR /% FRR /% Time /s
Fully 97.333 2.67 2.67 367.44
RMS 97.667 2.22 3.33 130.56

Table 6. Performance comparison among CDBN algorithm, BP, HMM, and DBN algorithms
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Table 6. Performance comparison among CDBN algorithm, BP, HMM, and DBN algorithms
Method ACC /% FAR /% FRR /% Time /s
BP 92.660 4.53 5.01 56.84
HMM 93.250 3.44 4.46 85.69
DBN 96.630 2.37 3.79 119.31
CDBN 97.667 2.22 3.33 130.56

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Yichao Zhang, Ziwen Sun. Identity Authentication for Smart Phones Based on an Optimized Convolutional Deep Belief Network[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081009

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