Invariant facial feature extraction using biologically inspired strategies

Xing Du; Weiguo Gong

doi:10.1117/1.3662410

1 December 2011 Invariant facial feature extraction using biologically inspired strategies

Xing Du, Weiguo Gong

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Optical Engineering, Vol. 50, Issue 12, 127205 (December 2011). https://doi.org/10.1117/1.3662410

Abstract

In this paper, a feature extraction model for face recognition is proposed. This model is constructed by implementing three biologically inspired strategies, namely a hierarchical network, a learning mechanism of the V1 simple cells, and a data-driven attention mechanism. The hierarchical network emulates the functions of the V1 cortex to progressively extract facial features invariant to illumination, expression, slight pose change, and variations caused by local transformation of facial parts. In the network, filters that account for the local structures of the face are derived through the learning mechanism and used for the invariant feature extraction. The attention mechanism computes a saliency map for the face, and enhances the salient regions of the invariant features to further improve the performance. Experiments on the FERET and AR face databases show that the proposed model boosts the recognition accuracy effectively.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Xing Du and Weiguo Gong "Invariant facial feature extraction using biologically inspired strategies," Optical Engineering 50(12), 127205 (1 December 2011). https://doi.org/10.1117/1.3662410

Published: 1 December 2011

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KEYWORDS

Feature extraction

Facial recognition systems

Image filtering

Data modeling

Bandpass filters

Databases

Autoregressive models

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