Age Classification using CNN

Overview

A Convolutional Neural Network designed from scratch trained using Keras framework that categorizes images of people based on their ages. The model categorizes the input image based on three categories- Young , Middle and Old.

Preprocessing

The following preprocessing was applied to each image:

Have trained the network on frontal faces images
Random crops of 64 × 64 pixels from the input image of random sizes
Randomly mirror images in each forward-backward training pass
Data Augmentation is used

Model Description

For Age Classification, following are the details of the model:

3x3 filter shape, 32 feature maps. Stride of 1 and 0 padding. Followed by: ReLU,Batch-Normalization,Max-Pool,Dropout of 0.25
3x3 filter shape, 64 feature maps. Followed by: Batch-Normalization
3x3 filter shape, 64 feature maps,stride 1 and padding 1. ReLU, Batch-Normalization,Max-Pool of size 2,Dropout of 0.25.
3x3 filter shape, 128 feature maps. Followed by: Batch-Normalization
3x3 filter shape, 128 feature maps. Followed by: Batch-Normalization
3x3 filter shape, 128 feature maps,stride 1 and padding 1. ReLU, Batch-Normalization,Max-Pool of size 2,Dropout of 0.25.
Fully connected layer of 512 neurons. Followed by : ReLU,Batch Normalization, Dropout = 0.5.
Last layer maps to the 3 classes for age

Trained with a learning rate of 0.01,Batch Size of 32 and with 75 to 100 epochs. Used Stochastic Gradient Descent (SGD) optimizer and 70% split of train and validation data. Used OpenCV library for image processing along with data visualization and augmentation.