Quantified Lockscreen: Integration of Personalized Facial Expression Detection and Mobile Lockscreen application for Emotion Mining and Quantified Self 


Vol. 42,  No. 11, pp. 1459-1466, Nov.  2015


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  Abstract

Lockscreen is one of the most frequently encountered interfaces by smartphone users. Although users perform unlocking actions every day, there are no benefits in using lockscreens apart from security and authentication purposes. In this paper, we replace the traditional lockscreen with an application that analyzes facial expressions in order to collect facial expression data and provide real-time feedback to users. To evaluate this concept, we have implemented Quantified Lockscreen application, supporting the following contributions of this paper: 1) an unobtrusive interface for collecting facial expression data and evaluating emotional patterns, 2) an improvement in accuracy of facial expression detection through a personalized machine learning process, and 3) an enhancement of the validity of emotion data through bidirectional, multi-channel and multi-input methodology.


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  Cite this article

[IEEE Style]

S. S. Kim, J. Park, W. Woo, "Quantified Lockscreen: Integration of Personalized Facial Expression Detection and Mobile Lockscreen application for Emotion Mining and Quantified Self," Journal of KIISE, JOK, vol. 42, no. 11, pp. 1459-1466, 2015. DOI: .


[ACM Style]

Sung Sil Kim, Junsoo Park, and Woontack Woo. 2015. Quantified Lockscreen: Integration of Personalized Facial Expression Detection and Mobile Lockscreen application for Emotion Mining and Quantified Self. Journal of KIISE, JOK, 42, 11, (2015), 1459-1466. DOI: .


[KCI Style]

김성실, 박준수, 우운택, "Quantified Lockscreen: 감정 마이닝과 자기정량화를 위한 개인화된 표정인식 및 모바일 잠금화면 통합 어플리케이션," 한국정보과학회 논문지, 제42권, 제11호, 1459~1466쪽, 2015. DOI: .


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