Face Detection

For Face Detection, the algorithm libraries used are

MTCNN (Multi-task Cascaded Convolutional Neural Networks)
RetinaFace

Notes:

MTCNN is a faster algorithm compared to RetinaFace.
It can be used in the case of images with many faces and where high accuracy of face detection is not expected.
MTCNN shows a lower accuracy in the case of tilted or distorted faces in images.
RetinaFace algorithm takes time to load but is more accurate with the face detection results.
From Face Detection results, you can download files for detected images and faces to your machine using the links provided in the output.

The available properties of Face Detection are as shown in the figure given below.

Using the Properties pane, you can select

- A suitable name for the task
- The input image(s) for analysis
- The Detector is the pre-trained model for analysis

In the output, you see highlighted faces to indicate that the faces are successfully detected.

Also, in the output, you see the detected faces individually displayed, beside the main image containing all the detected faces.

Notes:

From Face Detection results, you can download files for the images and each detected face. You can store them in your machine using the links provided on the Result page.