Abstract
On the wearable smartglasses device, this paper proposes a simple but practical 2D ear detection algorithm based on Arc Mask Superposition Operator (AMSO) and luminance density verification. In detail, in the first half phase of the proposed ear detection algorithm, a few ear candidates are extracted by AMSO followed by multilayer mosaic enhancement and orthogonal projection histogram analysis. Then, in the second half phase, the most likely ear candidate can be effectively verified by a straightforward comparison of luminance density. Experimental results show that the proposed ear detection algorithm without any detection false positive can achieve better hit rate and faster response performance than conventional AdaBoost-based ear detection algorithm. Afterward, Coherent Point Drift feature extraction algorithm on Android smartglasses device is also introduced. Implementation results show the real-time performance of the wearable ear recognition smartglasses is feasible for diverse biometric applications.
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Acknowledgments
This work was supported in part by Ministry of Science and Technology, Taiwan, under Grant MOST 106-2221-E-224-053.
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Lin, WS., Ho, C.C. (2019). Wearable Ear Recognition Smartglasses Based on Arc Mask Superposition Operator Ear Detection and Coherent Point Drift Feature Extraction. In: Chang, CY., Lin, CC., Lin, HH. (eds) New Trends in Computer Technologies and Applications. ICS 2018. Communications in Computer and Information Science, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-13-9190-3_83
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DOI: https://doi.org/10.1007/978-981-13-9190-3_83
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