IEEE Trans Image Process - Learning the spherical harmonic features for 3-D face recognition.

Tópicos

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Resumo

In this paper, a competitive method for 3-D face recognition (FR) using spherical harmonic features (SHF) is proposed. With this solution, 3-D face models are characterized by the energies contained in spherical harmonics with different frequencies, thereby enabling the capture of both gross shape and fine surface details of a 3-D facial surface. This is in clear contrast to most 3-D FR techniques which are either holistic or feature based, using local features extracted from distinctive points. First, 3-D face models are represented in a canonical representation, namely, spherical depth map, by which SHF can be calculated. Then, considering the predictive contribution of each SHF feature, especially in the presence of facial expression and occlusion, feature selection methods are used to improve the predictive performance and provide faster and more cost-effective predictors. Experiments have been carried out on three public 3-D face datasets, SHREC2007, FRGC v2.0, and Bosphorus, with increasing difficulties in terms of facial expression, pose, and occlusion, and which demonstrate the effectiveness of the proposed method.

Resumo Limpo

paper competit method d face recognit fr use spheric harmon featur shf propos solut d face model character energi contain spheric harmon differ frequenc therebi enabl captur gross shape fine surfac detail d facial surfac clear contrast d fr techniqu either holist featur base use local featur extract distinct point first d face model repres canon represent name spheric depth map shf can calcul consid predict contribut shf featur especi presenc facial express occlus featur select method use improv predict perform provid faster costeffect predictor experi carri three public d face dataset shrec frgc v bosphorus increas difficulti term facial express pose occlus demonstr effect propos method

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