A Robust Face Recognition Algorithm for Real-World Applications

We developed a local appearance-based face recognition algorithm using discrete cosine transform, which is a generic, robust, and fast face recognition algorithm that has been deployed for several real-world person identification applications. The proposed face recognition approach divides the input face image into local blocks and processes each local block using discrete cosine transform. The local representation provides robustness against appearance variations in local regions caused by factors such as facial occlusion or expression, whereas utilizing frequency information provides robustness against changes in illumination. The algorithm has been extensively tested both using standard benchmark databases —AR, CMU PIE, FRGC, Yale B, Extended Yale B— and using the data collected from real-world applications —person identification in smart rooms, entrance monitoring, visitor interface, person re-identification in TV series—. The experimental results show that, the algorithm can successfully handle facial appearance variations caused by uncontrolled recording conditions, expression, occlusion, and illumination. Moreover, the systems based on this algorithm have been found to work reliably under real-world conditions.
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Car Driver
(Source: flickr.com)
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