Our face is our password—face recognition promises to revolutionize the way we identify individuals in a nonintrusive and convenient manner. Even though research in face recognition has spanned over nearly three decades, only 2D systems, with limited adoption to practical applications, have been developed so far. The primary reason behind this is the low accuracy of 2D face recognition systems in the presence of: (i) pose variations between the gallery and probe datasets, (ii) variations in lighting, and (iii) variations in the presence of expressions and/or accessories. The above conditions generally arise when noncooperative subjects are involved, which is the very case that demands accurate recognition. Face recognition using 3D images was introduced in order to overcome these challenges. It was partly made possible by significant advances in 3D scanner technology. However, even 3D face recognition has faced significant challenges which have hindered its adoption for practical applications. The main problem of 3D face recognition is the high cost and fragility of 3D scanners. Over the last seven years, our research team has focused on exploring the usefulness of 3D data and the development of models for face recognition (under the general name URxD). In this chapter, we present advances that aid in overcoming the challenges encountered in 3D face recognition. First, we present a fully automatic 3D face recognition system, UR3D, which has been proven to be robust under variations in expressions. The fundamental idea of this system is the description of facial data using an Annotated Face Model (AFM). The AFM is fitted to the facial scan using
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