103D Face Recognition

10.1 Introduction

The automatic recognition of human faces has many potential applications in various fields including security and human–computer interaction. An accurate and robust face recognition system needs to discriminate between the faces of different people under variable conditions. The main challenge is that faces, from a general perspective, look similar and their differences can be very subtle. They all have the same structure and are composed of similar components (e.g. nose, eyes, and mouth). On the other hand, the appearance of the same face can considerably change under variable extrinsic factors, e.g. the camera position and the intensity and direction of light, and intrinsic factors such as the head position and orientation, facial expressions, age, skin color, and gender. On that basis, face recognition can be considered to be more challenging than the general object recognition problem discussed in Chapter 11.

Pioneer researchers initially focused on 2D face recognition, i.e. how to recognize faces from data captured using monocular cameras. They reported promising recognition results, particularly in controlled environments. With the recent popularity of cost‐effective 3D acquisition systems, face recognition systems are starting to benefit from the availability, advantages, and widespread use of 3D data. In this chapter, we review some of the recent advances in 3D face recognition. We will first present, in Section 10.2, the various ...

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