Skip to main content

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