Abstract
There is a growing interest in dimensionality reduction techniques for face recognition, however, the traditional dimensionality reduction algorithms often transform the input face image data into vectors before embedding. Such vectorization often ignores the underlying data structure and leads to higher computational complexity. To effectively cope with these problems, a novel dimensionality reduction algorithm termed distance adaptive tensor discriminative geometry preserving projection (DATDGPP) is proposed in this paper. The key idea of DATDGPP is as follows: first, the face image data are directly encoded in high-order tensor structure so that the relationships among the face image data can be preserved; second, the data-adaptive tensor distance is adopted to model the correlation among different coordinates of tensor data; third, the transformation matrix which can preserve discrimination and local geometry information is obtained by an iteration algorithm. Experimental results on three face databases show that the proposed algorithm outperforms other representative dimensionality reduction algorithms.
Highlights
Over the last decade face recognition has become one of the most active research areas in multimedia information processing due to the rapidly increasing requirements in many practical applications, such as identify authentication, information security, human‐computer interaction/communication and so on
Our empirical study on face recognition was conducted on three real‐world face databases: the Yale database, the Olivetti Research Laboratory (ORL) database and the PIE database from CMU
We proposed a novel distance adaptive tensor discriminative geometry preserving projection (DATDGPP) algorithm for face recognition
Summary
Over the last decade face recognition has become one of the most active research areas in multimedia information processing due to the rapidly increasing requirements in many practical applications, such as identify authentication, information security, human‐computer interaction/communication and so on. A number of manifold learning algorithms have been proposed to discover the geometric property of high‐dimensional data spaces and they have been successfully applied to face recognition. Manifold learning aims at discovering the geometric properties of the data space, such as its Euclidean embedding, intrinsic dimensionality, connected components, homology, etc. Grey‐level face images represent second‐order tensor data (matrices) and can be expanded to third‐order tensors by representing sets of images after Gabor filtering Such vectorization ignores the underlying data structure and often leads to the curse of the dimensionality dilemma and the small sample size problem. We propose a novel distance adaptive tensor manifold learning algorithm for face recognition. We discuss how to tensorize the discriminative geometry preserving projection which gives rise to distance adaptive tensor dimensionality reduction algorithm for face recognition.
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