Face detection using TinyFace

This package comes with a TinyFace face detector. The Original Model is ResNet101 from tinyface. Please check for more details on TinyFace. The model is converted into MxNet Interface and the code used to implement the model are from hr101_mxnet.

Implementation

See below for an example on how to use bob.ip.facedetect.tinyface.TinyFacesDetector:

import matplotlib.pyplot as plt
from bob.io.base import load
from bob.io.base.test_utils import datafile
from bob.io.image import imshow
from bob.ip.facedetect.tinyface import TinyFacesDetector
from matplotlib.patches import Rectangle

# load colored test image
color_image = load(datafile("test_image_multi_face.png", "bob.ip.facedetect"))
is_mxnet_available = True
try:
    import mxnet
except Exception:
    is_mxnet_available = False

if not is_mxnet_available:
    imshow(color_image)
else:

    # detect all faces
    detector = TinyFacesDetector()
    detections = detector.detect(color_image)

    imshow(color_image)
    plt.axis("off")

    for annotations in detections:
        topleft = annotations["topleft"]
        bottomright = annotations["bottomright"]
        size = bottomright[0] - topleft[0], bottomright[1] - topleft[1]
        # draw bounding boxes
        plt.gca().add_patch(
            Rectangle(
                topleft[::-1],
                size[1],
                size[0],
                edgecolor="b",
                facecolor="none",
                linewidth=2,
            )
        )

This face detector can be used for detecting single or multiple faces. If there are more than one face, the first entry of the returned annotation supposed to be the largest face in the image.

Fig. 1 Multiple faces are detected by TinyFace.