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Proceedings Paper

An embedded system for face classification in infrared video using sparse representation
Author(s): Antonio Saavedra M.; Jorge E. Pezoa; Payman Zarkesh-Ha; Miguel Figueroa
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Paper Abstract

We propose a platform for robust face recognition in Infrared (IR) images using Compressive Sensing (CS). In line with CS theory, the classification problem is solved using a sparse representation framework, where test images are modeled by means of a linear combination of the training set. Because the training set constitutes an over-complete dictionary, we identify new images by finding their sparsest representation based on the training set, using standard l1-minimization algorithms. Unlike conventional face-recognition algorithms, we feature extraction is performed using random projections with a precomputed binary matrix, as proposed in the CS literature. This random sampling reduces the effects of noise and occlusions such as facial hair, eyeglasses, and disguises, which are notoriously challenging in IR images. Thus, the performance of our framework is robust to these noise and occlusion factors, achieving an average accuracy of approximately 90% when the UCHThermalFace database is used for training and testing purposes. We implemented our framework on a high-performance embedded digital system, where the computation of the sparse representation of IR images was performed by a dedicated hardware using a deeply pipelined architecture on an Field-Programmable Gate Array (FPGA).

Paper Details

Date Published: 19 September 2017
PDF: 11 pages
Proc. SPIE 10396, Applications of Digital Image Processing XL, 103961N (19 September 2017); doi: 10.1117/12.2274305
Show Author Affiliations
Antonio Saavedra M., Univ. de Concepción (Chile)
Jorge E. Pezoa, Univ. de Concepción (Chile)
Payman Zarkesh-Ha, The Univ. of New Mexico (United States)
Miguel Figueroa, Univ. de Concepción (Chile)

Published in SPIE Proceedings Vol. 10396:
Applications of Digital Image Processing XL
Andrew G. Tescher, Editor(s)

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