Tied factors analysis for high-dimensional image feature extraction and recognition application

Feature extraction from images, which are typical of high dimensionality, is crucial to the recognition performance. To explore the discriminative information while depressing the intra-class variations due to variable illumination and view conditions, we propose a factor analysis framework for separate "content" from "style," identifying a familiar face seen under unfamiliar viewing conditions, classifying familiar poses presented in an unfamiliar face, estimating age across unfamiliar faces. The framework applies efficient algorithms derived from objective factor separating functions and space mapping functions, which can produce sufficiently expressive representations of feature extraction and dimensionality reduction. We report promising results on three different tasks in the high-dimensional image perceptual domains: face identification with two benchmark face databases, facial pose classification with a benchmark facial pose database, extrapolation of age to unseen facial image. Experimental results show that our approach produced higher classification performance when compared to classical LDA, WLDA, LPP, MFA, and DLA algorithms.