Introduction reconnaissance façiale

Face Detection

In the past few years, face recognition has become one of the most promising applications of computer vision. Face detection can be considered to be a substantial part of face recognition operations.

The method of face detection in pictures is complicated because, well, human faces are widely different from one another! They can have different poses, expressions, positions, orientation, skin color, have glasses or facial hair or not, etc. Then, there are also differences in camera gain, lighting conditions, and image resolution.

Face detection is the action of locating human faces in an image and optionally returning different kinds of face-related data.

How Do We Detect Faces?

There are a few methods that you can use to detect faces such as:

1. Knowledge-Based
2. Feature-Based

Knowledge-Based Detection

The knowledge-based method depends on a set of rules and is based on human knowledge to detect faces.

E.g. A face often appears with two eyes that are symmetric to each other, a nose and a mouth.

Pros

1. Easy to come up with simple rules.
2. Based on the coded rules, facial features in an input image are extracted first, and face candidates are identified.

Cons

1. Difficult to translate human knowledge into rules precisely.
   1. Detailed rules fail to detect faces, like the rule that, ‘A person must have two eyes’: According to this rule, no face will be identified in this image.
   2. General rules may find many false positives: Like there is an eye in a face. According to this, the face will be identified in this tattoo of a similar-looking eye.
2. This approach alone is insufficient and unable to find many faces in multiple images.

Feature-Based Detection

Feature-based detection uses what is known as face landmarks to detect faces.

Face landmarks are a set of easy-to-find points on a face, such as the pupils or the tip of the nose.

By default, there are 68 predefined landmark points. The following image shows all landmark points:

The feature-based method detects faces by extracting the face landmarks of the face.

Pros

1. Features are invariant to pose and orientation change.
2. This method has a higher chance of face detection.

Cons

1. Difficult to locate facial features due to noise in the images.
2. Difficult to detect features in complex backgrounds.

Conclusion

In this topic, we learned how to detect faces in images using different methods. In the next topic, we will look at the various functions for face detection in PictoBlox.