Human face images acquired using conventional 2D cameras may have inherent restrictions that hinder the inference of some specific information in the face. The low-cost depth sensors such as Microsoft Kinect introduced in late 2010 allow extracting directly 3D information, together with RGB color images. This provides new opportunities for computer vision and face analysis research. Although more accurate sensors for detailed facial image analysis are expected to be available soon (e.g. Kinect 2), this paper investigates the usefulness of the depth images provided by the current Microsoft Kinect sensors in different face analysis tasks. We conduct an in-depth study comparing the performance of the depth images provided by Microsoft Kinect sensors against RGB counterpart images in three face analysis tasks, namely identity, gender and ethnicity. Four local feature extraction methods are investigated for both face texture and shape description. Moreover, the two modalities (i.e. depth and RGB) are fused to gain insight into their complementarity. The experimental analysis conducted on two publicly available kinect face databases, EurecomKinect and Curtinfaces, yields into interesting results.
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