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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.

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.

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).

Also, face detector implements detectAsync() which allows you to asynchronously detect faces and their parameters on multiple images.

Note: Method detectAsync() is experimental, and it's interface may be changed in the future. Note: Method detectAsync() is not marked as noexcept and may throw an exception.

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.

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.

Also, face detector implements redetectAsync() which allows you to asynchronously redetect faces on multiple images based on the detection results for the previous frames.

Note: Method redetectAsync() is experimental, and it's interface may be changed in the future. Note: Method redetectAsync() is not marked as noexcept and may throw an exception.

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.