ODROID XU4 based implementation of decision level fusion approach for matching computer generated sketches

Implementing computer vision applications on energy efficient and powerful single board computer devices is a hot topic of research. ODROID-XU4 is one such latest single board computing device which is extremely energy efficient and powerful, having a small form factor when compared to any other ARM based embedded devices. It supports open source operations systems and runs a variety of Linux flavors including Ubuntu and various Android versions including Lollipop. Moreover, it supports USB 3.0, eMMC 5.0 and Gigabit Ethernet interfaces thus, making the device feasible to transfer data at a very high speed. The key contribution of this paper is we have developed a novel technique to match computer generated sketches with face photos and implemented it on ODROID XU4 single board computer which makes it feasible to be used in real-time. Human face is detected on the face photos using Viola Jones method. On the detected faces and computer generated sketches, feature extraction is performed using supervised auto-encoder to build deep architecture and matching is performed between computer generated sketches and face photos using Parallel Convolutional Neural Network (PCNN). Finally decision level fusion is performed to find the optimal matching result. In this study, the authors have performed pilot testing of their technique and results of their analysis are presented to the readers.