Fusing shape and texture features for pose-robust face recognition

Thorsten Gernoth; Rolf-Rainer Grigat

doi:10.1117/12.909070

2 February 2012 Fusing shape and texture features for pose-robust face recognition

Thorsten Gernoth, Rolf-Rainer Grigat

Proceedings Volume 8300, Image Processing: Machine Vision Applications V; 830006 (2012) https://doi.org/10.1117/12.909070
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

Unconstrained environments with variable ambient illumination and changes of head pose are still challenging for many face recognition systems. To recognize a person independent of pose, we separate shape from texture information using an active appearance model. We do not directly use the texture information from the active appearance model for recognition. Instead we extract local texture features from a shape and pose free representation of facial images. We use a smooth warp function to transform the images. We compensate also the shape information for head pose changes and fuse the results of separate classiers for shape features and local texture features. We analyze the inuence of the individual contributions of shape and texture information on the recognition performance. We show that fusing shape and texture information can boost the recognition performance in an access control scenario.

Citation Download Citation

Thorsten Gernoth and Rolf-Rainer Grigat "Fusing shape and texture features for pose-robust face recognition", Proc. SPIE 8300, Image Processing: Machine Vision Applications V, 830006 (2 February 2012); https://doi.org/10.1117/12.909070

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KEYWORDS

Facial recognition systems

Feature extraction

Head

Systems modeling

Current controlled current source

Electronic imaging

Machine vision

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