Face Recognition Model Using Transfer Learning

Face Recognition is a method of perceiving a human face through innovation. A face Recognition framework utilizes biometrics to delineate highlights from a photo or video. It compares the information with a database of known faces to find a match.

What is Transfer Learning?

Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task.

It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks given the vast compute and time resources required to develop neural network models on these problems and from the huge jumps in skill that they provide on related problems.

Required Python Libraries

OpenCV

numpy

tensorflow

Let’s start with coding

Step 1. Collecting own dataset

I write a code in python which capture human photo and save in a folder.

Run this program for collecting datasets.

Step 2. Create Model

Step 3. Give data to our model

Step 4. Train model and save the model

Step 5. Now we train our model now we have to test our model

Here you see it give an accurate result:

For full code, you can visit my GitHub repo:

ashut08/Face_Recognization_Mobilenet

Thanks for reading!