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Proceedings Paper

Heterogeneous sharpness for cross-spectral face recognition
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Paper Abstract

Matching images acquired in different electromagnetic bands remains a challenging problem. An example of this type of comparison is matching active or passive infrared (IR) against a gallery of visible face images, known as cross-spectral face recognition. Among many unsolved issues is the one of quality disparity of the heterogeneous images. Images acquired in different spectral bands are of unequal image quality due to distinct imaging mechanism, standoff distances, or imaging environment, etc. To reduce the effect of quality disparity on the recognition performance, one can manipulate images to either improve the quality of poor-quality images or to degrade the high-quality images to the level of the quality of their heterogeneous counterparts. To estimate the level of discrepancy in quality of two heterogeneous images a quality metric such as image sharpness is needed. It provides a guidance in how much quality improvement or degradation is appropriate. In this work we consider sharpness as a relative measure of heterogeneous image quality. We propose a generalized definition of sharpness by first achieving image quality parity and then finding and building a relationship between the image quality of two heterogeneous images. Therefore, the new sharpness metric is named heterogeneous sharpness. Image quality parity is achieved by experimentally finding the optimal cross-spectral face recognition performance where quality of the heterogeneous images is varied using a Gaussian smoothing function with different standard deviation. This relationship is established using two models; one of them involves a regression model and the other involves a neural network. To train, test and validate the model, we use composite operators developed in our lab to extract features from heterogeneous face images and use the sharpness metric to evaluate the face image quality within each band. Images from three different spectral bands visible light, near infrared, and short-wave infrared are considered in this work. Both error of a regression model and validation error of a neural network are analyzed.

Paper Details

Date Published: 1 May 2017
PDF: 11 pages
Proc. SPIE 10202, Automatic Target Recognition XXVII, 102020Q (1 May 2017); doi: 10.1117/12.2261984
Show Author Affiliations
Zhicheng Cao, West Virginia Univ. (United States)
Natalia A. Schmid, West Virginia Univ. (United States)


Published in SPIE Proceedings Vol. 10202:
Automatic Target Recognition XXVII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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