Abstract

Smartphone applications became very popular nowadays as they provide useful functionalities to our daily lives over ordinary voice services. They offer a small but powerful computing platform where intelligent algorithms can be coded into some live-saving products which profoundly impact our daily lifestyles. A mobile fatigue detection system which is proposed in this study is an important life-saving application running on smartphone. Mobile detection system faces technological challenges such as: (i) embracing relatively low-resolution images in image recognition; (ii) supporting fast response time given a low-power CPU in comparison to a desktop computer; and (iii) demanding for high prediction accuracy by light-weight machine learning algorithms, as the software programs embedded in smartphones is resource constrained. Solutions with respect to indoor facial profiling system which are mainly based on progressive locating method for eye detection are discussed in this study. Acceptable experimental results in terms of eye detection rate and driver fatigue detection in different situations are presented.

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