Deep Learning project using Keras and TensorFlow
DESCRIPTION
Facial recognition is a biometric alternative that measures unique characteristics of a human face. Applications available today include flight check in, tagging friends and family members in photos, and “tailored” advertising. You are a computer vision engineer who needs to develop a face recognition programme with deep convolutional neural networks. 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 images per person. The images were taken at different times, lighting and facial expressions. The faces are in an upright position in frontal view, with a slight left-right rotation. Link to the Dataset: https://www.dropbox.com/s/i7uzp5yxk7wruva/ORL_faces.npz?dl=0 Prerequisites: Keras Scikit Learn Steps to be followed:
- Input the required libraries
- Load the dataset after loading the dataset, you have to normalize every image.
- Split the dataset
- Transform the images to equal sizes to feed in CNN
- Build a CNN model that has 3 main layers: i. Convolutional Layer ii. Pooling Layer iii. Fully Connected Layer
- Train the model
- Plot the result
- Iterate the model until the accuracy is above 90%