An empirical approach for automatic face clustering on personal lifelogging images

Subbaraju, Vigneshwaran; Xu, Qianli; Mandal, Bappaditya; Li, Liyuan; Lim, Joo-Hwee

Abstract

Life-logging applications generate a vast amount of personalized data that provides vital insights into the user's daily life. One such key insight is the people whom the user has come across/interacted with during regular life. This can be obtained from the faces extracted from images acquired by a wearable life-logging camera. However, manual inspection and tagging of the life-logging images is cumbersome and highly subjective. Therefore, in this paper, a fully automatic method to extract and cluster the faces from the images obtained from a life-logging camera is designed and evaluated. It is shown that such a practical system designed using commercial off-the-shelf devices and commercially available face recognition APIs is able to obtain human like precision, while the recall may be lower compared to human performance.

Citation

Subbaraju, V., Xu, Q., Mandal, B., Li, L., & Lim, J.-H. (2017, August). An empirical approach for automatic face clustering on personal lifelogging images. Presented at 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), Singapore

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP)
• Start Date: Aug 4, 2017
• End Date: Aug 6, 2017
• Online Publication Date: Nov 30, 2017
• Publication Date: 2017-08
• Deposit Date: Nov 17, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• DOI: https://doi.org/10.1109/siprocess.2017.8124519
• Public URL: https://keele-repository.worktribe.com/output/636541
• Publisher URL: https://ieeexplore.ieee.org/document/8124519