Can an Algorithm Recognize Montage Portraits as Human Faces? [In the Spotlight]

The face is one of the most important features of a human being, and its recognition is essential. Humans have to know immediately who they are facing-an enemy or a friend? We are most likely genetically coded not only to recognize faces, but also to extract the characteristics of expressions of faces for survival. The need to recognize faces probably accelerated the evolution of human intelligence. Now, due to the significant leaps that computing power took over the past decade, the time has come for machines/computers to mimic the same process-the ability to achieve reliable face recognition as successful as human beings or even better. Face recognition is, of course, a fertile ground for the development of new algorithms and applications in a myriad of fields. For example, CNN recently reported [1] that Facebook was testing a new feature that uses face recognition to help in the tagging of photos. For over a decade, there has been an increasing interest in face recognition in diverse fields such as pattern recognition, computer vision, telecommunications, video, security and Internet applications, and cognitive psychology. Among various face recognition algorithms developed, they are mainly classified into two groups: pose dependent and pose invariant [2]. Pose-dependent algorithms rely on twodimensional (2-D) images of different poses of faces, while pose-invariant techniques are based on three-dimensional (3-D) models. Here, without discussing the details of such approaches, let us ask a simple question: How does an artist or a caricaturist capture the characteristics of faces and with simple line drawings makes us successfully recognize faces, in many cases better than the full image of a person? If the answer can be found, we may come up with better face recognition methods.