Significance of image representation for face verification |
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Authors: | Anil Kumar Sao B Yegnanarayana B V K Vijaya Kumar |
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Affiliation: | (1) Indian Institute of Technology Madras, Chennai, 600036, Tamil Nadu, India;(2) International Institute of Information Technology, Hyderabad, 500032, Andhra Pradesh, India;(3) Carnegie Mellon University, Pittsburgh, PA 15213, USA |
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Abstract: | In this paper we discuss the significance of representation of images for face verification. We consider three different representations,
namely, edge gradient, edge orientation and potential field derived from the edge gradient. These representations are examined
in the context of face verification using a specific type of correlation filter, called the minimum average correlation energy
(MACE) filter. The different representations are derived using one-dimensional (1-D) processing of image. The 1-D processing
provides multiple partial evidences for a given face image, one evidence for each direction of the 1-D processing. Separate
MACE filters are used for deriving each partial evidence. We propose a method to combine the partial evidences obtained for
each representation using an auto-associative neural network (AANN) model, to arrive at a decision for face verification.
Results show that the performance of the system using potential field representation is better than that using the edge gradient
representation or the edge orientation representation. Also, the potential field representation derived from the edge gradient
is observed to be less sensitive to variation in illumination compared to the gray level representation of images. |
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Keywords: | Face verification 1-D image processing Minimum average correlation energy (MACE) filter Auto-associative neural network (AANN) |
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