4 3D face recognition
4.1 3D face recognition methods

3D face recognition in comparison to 2D face recognition profits from bigger information flow about the face characteristics. Similarly, both approaches need basic preprocessing like face-size normalization, rotation to a neutral position etc. Added information not only about the 2D shape but about the depth analysis offers rich source of information not captured in 2D images. Main advantages to 2D face analysis are:

- Not affected by illumination variations or use of cosmetics
- Less sensitive to appearance variations
- Easier to handle pose variations
- Projective nature of 2D images
- Simplifies face & facial feature detection, pose estimation & pose compensation

The selected basic 3D face recognition methods:

- Surface-based 3D face recognition - this approach is based on of classic 3D object recognition techniques (Fig. 3.1). There are various types of recognition methods based on:
  - Use of local curvature features, which are rotation invariant (e.g. curve of the face profile)
  - Use of point-to-point matching (polygon of several significant face points)

Fig. 3.1 – Surface-based 3D recognition

- Appearance-based 3D face recognition - this method deals with eigenfaces and fisherfaces. Accurate alignment of probe and images in database is required. Facial features such as eyes, mouth, etc. are localized and utilized for recognition. The method is easy to implement and is not time consuming (Fig. 3.2).

Fig. 3.2 – Appearance-based 3D recognition

- Model- based 3D face recognition - this method is based on analysis by synthesis method, where the 3D morphable and annotated face model is produced, which is then compared with models in database. The methods is not suitable for real time application (Fig. 3.3).

Fig. 3.3 – Model-based 3D recognition