Face feature extraction method based on heterogeneous tensor decomposition

Abstract

The invention discloses a face feature extraction method based on heterogeneous tensor decomposition. The method includes: using an arbitrary camera array to collect color views of different faces at different visual angles, and obtaining an initial view set of the faces after image processing such as gray-scale transformation and normalization; extracting images of the initial view set, and carrying out sequential stacking to form a third-order tensor, wherein a third-order dimension of the formed third-order tensor corresponds to the total number of the face images; carrying out TUCKER decomposition on the formed third-order tensor to obtain a kernel tensor, a first pattern factor matrix, a second pattern factor matrix and a third pattern factor matrix, and updating the same; judging whether the kernel tensor converges; and decomposing the obtained third pattern factor matrix Z, wherein high-dimensional face data are mapped to the pattern factor matrix of low-dimensional feature subspace, and thus the third pattern factor matrix Z is finally extracted face features. According to the method, automatic extraction of the face image features is realized, tedious steps of traditional feature extraction are avoided, and feature extraction speed is increased.

Description

technical field [0001] The invention relates to a method for extracting human face features. In particular, it involves a face feature extraction method based on heterogeneous tensor decomposition. Background technique [0002] Data from multiple sources are arranged to form a tensor [1]. In order to dig deep into the intrinsic information of the tensor, it is necessary to decompose the tensor. Tensor factorization is an emerging powerful tool for exploring multidimensional data. The TUCKER and PARAFAC models are the most basic models of tensor decomposition. Tensor decomposition extracts and classifies features by capturing the multiple linear and multi-angle structures of large-scale multi-dimensional datasets. Tensor decomposition is widely used in medicine and neuroscience, social network analysis, computer vision [2], recommendation system and other fields. [0003] Supervised and unsupervised dimensionality reduction and feature extraction based on tensor representa...

AI Technical Summary

Problems solved by technology

Traditional algorithms such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), etc. treat multidimensional data as matrices or vectors, and cannot make good use of the structural information inside the data.

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