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Proceedings of the 2022 International Conference on Research in Management & Technovation

Annals of Computer Science and Information Systems, Volume 34

Face Recognition Technology Using the Fusion of Local Descriptors

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DOI: http://dx.doi.org/10.15439/2022M6308

Citation: Proceedings of the 2022 International Conference on Research in Management & Technovation, Viet Ha Hoang, Vijender Kumar Solanki, Nguyen Thi Hong Nga, Shivani Agarwal (eds). ACSIS, Vol. 34, pages 227–231 ()

Full text

Abstract. Local phase quantization (LPQ) descriptor, first introduced by Ojansivu and Heikkila (2008), has successfully been applied in face recognition systems. In this paper, we combine local intensity area descriptor (LIAD), which was first introduced by Tran (2017), with LPQ descriptor to develop robust face recognition systems using LPQ descriptor. Face images were first encoded by LIAD as a noise and dimensionality reduction step. After that, the resulting images were presented through LPQ as a feature extraction step. A nearest neighbor method with chi-square measure is used in classification. Two famous datasets (the ORL Database of Faces and FERET) were used in experiments. The results confirmed that our proposed approach reached mean recognition accuracies that are 0.17\% ÷ 7.7\% better compared to five conventional descriptors (LBP, LDP, LDN, LTP, and LPQ).

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