Smartface: Efficient Face Detection On Smartphones For Wireless On-Demand Emergency Networks

To support the search for missing persons during a natural disaster, photos taken by smartphone users staying inside the disaster area can be shared over wireless on-demand emergency networks formed by mobile devices. Detecting faces of persons in images and transmitting only the extracted faces can reduce the amount of transmitted data. However, executing common face detection algorithms on mobile devices is challenging, since these algorithms were not designed to cope with the devices' limited resources. In this paper, we present a novel approach to perform face detection locally on mobile devices in an efficient manner. The approach relies on a two-stage combination of existing face detection algorithms, enhanced by region of interest selection, color space/depth reduction, resolution scaling, face size definition, image scaling, image cropping, and bounding box scaling. Experimental results indicate that the proposed approach improves both the overall face detection rate and the overall runtime compared to the individual face detection algorithms used alone, and also reduces the amount of data that needs to be stored on disk and sent over the network.