Face identification in near IR-videos using smart eye tracking profiles

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/212358
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Type: Examensarbete för masterexamen
Master Thesis
Title: Face identification in near IR-videos using smart eye tracking profiles
Authors: Babic, Kenan
Walterson, Fredrik
Abstract: In this thesis we have evaluated the possibility of using data extracted from Smart Eye tracking pro les for the task of driver identi cation in near-IR videos using face recognition techniques. Two texture-based methods are presented to solve this task. The rst method utilizes local binary patterns (LBP) combined with local discriminant analysis (LDA) to identify subjects whilst the second method investigates brute-force template matching for identi cation purposes. The LBP-method was evaluated for both closed-set and open-set identi cation and achieved good results. In the closed-set scenario, a recognition rate of 98% was reached on our own data set of 47 videos containing 24 di erent subjects, despite the fact that the subjects had modi ed their appearances in half of the videos. The LBP-method is fast and our results demonstrates the LBP-method's ability to handle partial facial occulusions and appearance variations. The tem- plate matching method showed decent results with a peak recognition rate of 81% in the closed-set scenario. However, the method proved very time-consuming due to large number of templates in the pro les, limiting the method's viability in real-time applications. Future re nements and extensions are proposed for both methods.
Keywords: Elektroteknik och elektronik;Electrical Engineering, Electronic Engineering, Information Engineering
Issue Date: 2015
Publisher: Chalmers tekniska högskola / Institutionen för signaler och system
Chalmers University of Technology / Department of Signals and Systems
Series/Report no.: Ex - Institutionen för signaler och system, Chalmers tekniska högskola : EX001/2015
URI: https://hdl.handle.net/20.500.12380/212358
Collection:Examensarbeten för masterexamen // Master Theses

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