Fusion of 2.5D Face Recognition through Extreme Learning Machine via Manifold Flattening

Abstract

A flexible feature descriptor, Gabor-based Region Covariance Matrix (GRCM), embeds the Gabor features into the covariance matrix has emerged in face recognition. GRCM locates on Tensor manifold, a non-Euclidean space, utilises distance measures such as Affine-invariant Riemannian Metric (AIRM) and Log-Euclidean Riemannian Metric (LERM) to calculate the distance between two covariance matrices. However, these distance measures are computationally expensive. Therefore, a machine learning approach via manifold flattening is proposed to alleviate the problem. Besides, several feature fusions that integrate the 2.5D partial data and 2D texture image are investigated to boost the recognition rate. Experimental results have exhibited the effectiveness of the proposed method in improving the recognition rate for 2.5D face recognition.