Table 1 The optimized setting of our CRCNN method
From: A novel comparative deep learning framework for facial age estimation
Deep architecture’s parameters | Optimized value |
|---|---|
Fusion | Early |
Number of baseline samples | 5 |
Region detection | Yes |
Number of convolutional layers | 3 |
Number of locally-connected layers | 0 |
Number of fully-connected layers | 1 |
Batch size | 32 |
Activation function | reLU |
Dropout | 0.5 |
Learning rate | 1 |
Momentum | 0.9 |
Weight penalty | 1e-2 |