Robust Face Representation Using Hybrid Spatial Feature Interdependence Matrix

Anbang Yao

doi:10.1109/TIP.2013.2246523

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Robust Face Representation Using Hybrid Spatial Feature Interdependence Matrix

2013-08-01

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Anbang Yao *

Shan Yu

DOI：10.1109/TIP.2013.2246523

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Abstract

En 中文

A key issue in face recognition is to seek an effective descriptor for representing face appearance. In the context of considering the face image as a set of small facial regions, this paper presents a new face representation approach coined spatial feature interdependence matrix (SFIM). Unlike classical face descriptors which usually use a hierarchically organized or a sequentially concatenated structure to describe the spatial layout features extracted from local regions, SFIM is attributed to the exploitation of the underlying feature interdependences regarding local region pairs inside a class specific face. According to SFIM, the face image is projected onto an undirected connected graph in a manner that explicitly encodes feature interdependence-based relationships between local regions. We calculate the pair-wise interdependence strength as the weighted discrepancy between two feature sets extracted in a hybrid feature space fusing histograms of intensity, local binary pattern and oriented gradients. To achieve the goal of face recognition, our SFIM-based face descriptor is embedded in three different recognition frameworks, namely nearest neighbor search, subspace-based classification, and linear optimization-based classification. Extensive experimental results on four well-known face databases and comprehensive comparisons with the state-of-the-art results are provided to demonstrate the efficacy of the proposed SFIM-based descriptor.

Keywords:

Dimension reduction

face recognition

linear regression

local binary pattern

nearest neighbor search

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