FGIDetect

Face and Gesture Image Detection FGIDetect is a project to detect face and guesture using camera capture technology.

Requirements

• Python 3.6.1 OpenCV 3.4.1 Keras 2.0.2 Tensorflow 1.2.1 Theano 0.9.0
• Windows or Linux (macOS not officially supported, but might work)
• Suggestion: Better to download Anaconda as it will take care of most of the other packages and easier to setup a virtual workspace to work with multiple versions of key packages like python, opencv etc.

Repo contents

• ui_setup.py: The main script launcher. This file contains all the code for UI options and OpenCV code to capture camera contents. This script internally calls interfaces to gesture_recognize.py and camera_face.py.
• gesture_recognize.py: This script file holds all the CNN specific code to create CNN model, load the weight file (if model is pretrained), train the model using image samples present in ./train_set2, visualize the feature maps at different layers of NN (of pretrained model) for a given input image present in ./train_set2 folder.
• camera_face.py: This script file holds the face recognition code to recognize face through camera video, load the user name using face image samples present in ./dataset folder.

Installation Options:

• install this module from pypi using pip3 (or pip2 for Python 2):

  pip3 install face_recognition

Usage

• On Windows

  python ui_setup.py

features

This application comes with CNN model to recognize upto 4 pretrained gestures:

• OK
• One
• Two
• Five

This application provides following functionalities:

• Prediction : Which allows the app to guess the user's gesture against pretrained gestures. App can dump the prediction data to the console terminal or to a json file directly which can be used to plot real time prediction bar chart (you can use my other script
• New Training : Which allows the user to retrain the NN model. User can change the model architecture or add/remove new gestures. This app has inbuilt options to allow the user to create new image samples of user defined gestures if required.
• Visualization : Which allows the user to see feature maps of different NN layers for a given input gesture image. Interesting to see how NN works and learns things.