Comparison of Face Detection Algorithms on Mobile Devices

Abstract

Face detection is a fundamental task for many computer vision applications such as access control, security, advertisement, automatic payment, and healthcare. Due to technological advances mobile robots are becoming increasingly common in such applications (e.g. healthcare and security robots) and consequently there is a need for efficient and effective face detection methods on such platforms. Mobile robots have different hardware configurations and operating conditions from desktop applications, e.g. unreliable network connections and the need for lower power consumption. Hence results for face detection methods on desktop platforms cannot be directly translated to mobile platforms.We compare four common face detection algorithms, Viola-Jones, HOG, MTCNN and MobileNet-SSD, for use in mobile robotics using different face data bases. Our results show that for a typical mobile configuration (Nvidia Jetson TX2) Mobile-NetSSD performed best with 90% detection accuracy for the AFW data set and a frame rate of almost 10 fps with GPU acceleration. MTCNN had the highest precision and was superior for more difficult face data sets, but did not achieve real-time performance with the given implementation and hardware configuration.