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In this chapter, we propose an ensemble of face detectors for maximizing the number of true positives found by the system. Unfortunately, combining different face detectors increases both the number of true positives and false positives. To overcome this difficulty, several methods for reducing false positives are tested and proposed. The different filtering steps are based on the characteristics of the depth map related to the subwindows of the whole image that contain the candidate faces. The most simple and easiest criteria to use, for instance, is to filter the candidate face region by considering its size in metric units.\ud \ud The experimental section demonstrates that the proposed set of filtering steps greatly reduces the number of false positives without decreasing the detection rate. The proposed approach has been validated on a dataset of 549 images (each including both 2D and depth data) representing 614 upright frontal faces. The images were acquired both outdoors and indoors, with both first and second generation Kinect sensors. This was done in order to simulate a real application scenario. Moreover, for further validation and comparison with the state-of-the-art, our ensemble of face detectors is tested on the widely used BioID dataset where it obtains 100 % detection rate with an acceptable number of false positives.\ud \ud A MATLAB version of the filtering steps and the dataset used in this paper will be freely available from http://​www.​dei.​unipd.​it/​node/​2357

This paper proposes a novel approach for the classification of 3D shapes exploiting deep learning techniques. The proposed algorithm starts by constructing a set of depth maps by rendering the input 3D shape from different viewpoints. Then the depth maps are fed to a multi-branch Convolutional Neural Network. Each branch of the network takes in input one of the depth maps and produces a classification vector by using 5 convolutional layers of progressively reduced resolution. The various classification vectors are finally fed to a linear classifier that combines the outputs of the various branches and produces the final classification. Experimental results on the Princeton ModelNet database show how the proposed approach allows to obtain a high classification accuracy and outperforms several state-of-the-art approaches.