Fusion of Color and Depth Information for Facial Recognition using a Multi Perspective Approach

摘要: Facial-recognition is an explored and demanding task. Previously, mostly color (RGB) images were used to tackle it. Recently, advances in 3D scanners have been providing extra facial information. This new information improves the performance of current facial recognition architectures. In this research, both RGB and depth image information were utilized for addressing the problem of facial-recognition and by characterizing each image with the use of multi-perspective-approach (MPA). Data were combined from different textural-image-descriptors (TIDs) while keeping the most relevant features. Feature vectors resulting from such combinations were entered into a random-forest-classifier (RFC) to obtain a comparative analysis through the EURECOM facial dataset. The outcomes of our case-studies are comprehensively elaborated in this paper.