Facial Recognition with Deep Learning in Keras Using CNN

Facial recognition is a biometric alternative that measures unique characteristics of a humanface. Applications available today include flight check in, tagging friends and family members in photos, and “tailored” advertising.

Problem Objective:

Use a deep convolutional neural network to perform facial recognition using Keras.

Dataset Details:

ORL face database composed of 400 images of size 112 x 92. There are 40 people, 10 imagesper person. The images were taken at different times, lighting and facial expressions. The facesare in an upright position in frontal view, with a slight left-right rotation.

Steps to be followed:

1. Input the required libraries
2. Load the dataset. Normalize every image.
3. Split the dataset
4. Transform the images to equal sizes to feed in CNN5. Build a CNN model that has 3 main layers: i. Convolutional Layer ii. Pooling Layer iii. Fully Connected Layer
5. Train the model
6. Plot the result
7. Iterate the model until the accuracy is above 90%
   

Setup and Installation:

pip install --upgrade pip
pip install -r requirements.txt
pip list