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Eye detection and localization are basic steps in many computer systems aimed at human fatigue monitoring. In this paper we evaluate performance of two sparse image descriptors for eye detection in the long-range IR spectrum. In the training phase, sparse descriptors of the training images are computed and used to create features vocabulary. Final detections are done using bag-of-words approach and additional heuristic for geometric constraints. Several thermal video sequences were recorded to allow for quantitive analysis of this approach. Experimental results show that our method achieves high accuracy in real conditions.

Comparison of Sparse Image Descriptors for Eyes Detection in Thermal Images