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Detection facility#

Overview#

Object detection facility is responsible for quick and coarse detection tasks, like finding a face in an image.

Detection structure#

The detection structure represents an images-space bounding rectangle of the detected object as well as the detection score.

Detection score is a measure of confidence in the particular object classification result and may be used to pick the most "confident" face of many.

Note: Detection score is the measure of classification confidence and not the source image quality. While the score is related to quality (low-quality data generally results in a lower score), it is not a valid metric to estimate the visual quality of an image. Special estimators exist to fulfill this task (see section "Image quality estimation" in chapter "Parameter estimation facility" for details).

Face Detection#

Object detection is performed by the IDetector object. The function of interest is detect(). It requires an image to detect on and an area of interest (to virtually crop the image and look for faces only in the given location).

Image coordinate system#

The origin of the coordinate system for each processed image is located in the upper left corner.

Source image coordinate system
Source image coordinate system

Face detection#

When a face is detected, a rectangular area with the face is defined. The area is represented using coordinates in the image coordinate system.

When a part of a face is outside of the frame, the detection area will also be beyond the frame borders. Hence coordinates of the detection area may have the following values:

  • When the face is beyond the left or the upper border of the frame, the detection coordinates will have negative values;

In the image below, the upper left detection point is outside of the frame. Hence the X and Y coordinates of the upper left detection point have negative values.

Upper left detection point is outside of the frame
Upper left detection point is outside of the frame
  • When the face is beyond the right or the lower border of the frame, the detection coordinates will have positive values, but their values will exceed the image size.

In the image below, the X coordinate is equal to X + n, where n is the length of the zone that exceeds the image frame size.

Lower right detection point is outside of the frame
Lower right detection point is outside of the frame

NOTE! You must consider this feature when processing images to properly process the received coordinates.

A code example for detection cropping is given below. 

const fsdk::Rect brect = detection.rect & image.getRect();

detection - face detection. image - source image.

Redetect method#

Face detector implements redetect() method which is intended for face detection optimization on video frame sequences. Instead of doing full-blown detection on each frame, one may detect() new faces at a lower frequency (say, each 5th frame) and just confirm them in between with redetect(). This dramatically improves performance at the cost of detection recall. Note that redetect() updates face landmarks as well.

Detector works faster with larger value of minFaceSize.

Face Alignment#

Five landmarks#

Face alignment is the process of special key points (called "landmarks") detection on a face. FaceEngine does landmark detection at the same time as the face detection since some of the landmarks are by-products of that detection.

At the very minimum, just 5 landmarks are required: two for eyes, one for a nose tip and two for mouth corners. Using these coordinates, one may warp the source photo image (see Chapter "Image warping") for use with all other FaceEngine algorithms.

All detector may provide 5 landmarks for each detection without additional computations.

Typical use cases for 5 landmarks:

  • Image warping for use with other algorithms:

    • Quality and attribute estimators;
    • Descriptor extraction.
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