Representation Plurality and Fusion for 3-D Face Recognition

In this paper, we present an extensive study of 3-D face recognition algorithms and examine the benefits of various score-, rank-, and decision-level fusion rules. We investigate face recognizers from two perspectives: the data representation techniques used and the feature extraction algorithms that match best each representation type. We also consider novel applications of various feature extraction techniques such as discrete Fourier transform, discrete cosine transform, nonnegative matrix factorization, and principal curvature directions to the shape modality. We discuss and compare various classifier combination methods such as fixed rules and voting- and rank-based fusion schemes. We also present a dynamic confidence estimation algorithm to boost fusion performance. In identification experiments performed on

• Type: Journal
• Year: 2008
• Where: TSMC
• Authors: Berk Gökberk, Helin Dutagaci, A. Ulas, Lale Akarun, Bülent Sankur
• Keywords: Decision-level Fusion Rules, Face Recognition Algorithms, Feature Extraction, TSMC 2008