Analysis of RGB-D camera technologies for supporting different facial usage scenarios

AbstractRecently a wide variety of applications has been developed integrating 3D functionalities. Advantages given by the possibility of relying on depth information allows the developers to design new algorithms and to improve the existing ones. In particular, for what concerns face morphology, 3D has led to the possibility to obtain face depth maps highly close to reality and consequently an improvement of the starting point for further analysis such as Face Detection, Face Authentication, Face Identification and Face Expression Recognition. The development of the aforementioned applications would have been impossible without the progress of sensor technologies for obtaining 3D information. Several solutions have been adopted over time. In this paper, emphasis is put on passive stereoscopy, structured light, time-of-flight (ToF) and active stereoscopy, namely the most used technologies for the cameras design and fulfilment according to the literature. The aim of this article is to investigate facial applications and to examine 3D camera technologies to suggest some guidelines for addressing the correct choice of a 3D sensor according to the application that has to be developed

Automatic 3D foetal face model extraction from ultrasonography through histogram processing

Ultrasound is by far the most adopted method for safe screening and diagnosis in the prenatal phase, thanks to its non-harmful nature with respect to radiation-based imaging techniques. The main drawback of ultrasound imaging is its sensitivity to scattering noise, which makes automatic tissues segmentation a tricky task, limiting the possible range of applications. An algorithm for automatically extracting the facial surface is presented here. The method provides a comprehensive segmentation process and does not require any human intervention or training procedures, leading from the output of the scanner directly to the 3D mesh describing the face. The proposed segmentation technique is based on a two-step statistical process that relies on both volumetric histogram processing and 2D segmentation. The completely unattended nature of such a procedure makes it possible to rapidly populate a large database of 3D point clouds describing healthy and unhealthy faces, enhancing the diagnosis of rare syndromes through statistical analyses