omarabid59/face-recognition-pipeline

A robust face recognition pipeline using FaceNet as a backend. A moving average algorithm is used to give a better prediction of the person being detected.

Face-Recognition-Pipeline

A robust face recognition pipeline using FaceNet as a backend. A moving average algorithm is used to give a better prediction of the person being detected.

Quick Start Guide

Grab the code from this GitHub repository.

git clone --recurse-submodules [email protected]:omarabid59/Face-Recognition-Pipeline.git

Import the FaceDTCM package

import FaceDTCM.utils as dtcmUtils
import FaceDTCM.Pipeline as faceDtcmPipeline

Initialize the web camera so we can get video input to feed into our pipeline. Initializing the camera returns a pointer to a thread which holds the webcam data.
thread_img = dtcmUtils.init_camera()

Specify the path to our Neural Network and SVM classifier.

SVM_CLASSIFIER = "/path/to/svm"
NN_CLASSIFIER_PATH = "/path/to/nn/classifier"

Initialize the Face Detection, Tracking, Recognition and Memory pipeline

face_pipeline = faceDtcmPipeline(thread_img.image_data,
                        SVM_CLASSIFIER,
                        NN_CLASSIFIER_PATH,
                        detector_thresh = 0.5,
                        recognition_thresh=0.5)

Run the Pipeline

The simplest way to get the pipeline running is to start all processes. We can do this with the startAll() function.

face_pipeline.startAll()

Visualize the results

faceFrame = draw_face_frame()
while True:
    # Get the webcam image
    image_np = thread_img.image_data.image_np.copy()
    results = face_pipeline.detect_and_recognize()

    image_np = faceFrame.drawBoxes(image_np, results.bbs,results.scores,results.persons)
    
    cv2.imshow("Frame",image_np);


    cv2.destroyAllWindows()

TODO: Move to a TensorFlow.js version.