An Optimized Face Recognition for Edge Computing In this paper, we present an optimized face recognition algorithm for edge computing. We replace the original VGG16 with a more compact MobileNet to obtain a much smaller SSD model for face detection, and use a traditional 2D face key point detection to obtain an aligned face image. In face representation, we retrain the SphereFace using FP16 instead of FP32 to further reduce resource consumption. We then build the face detection and face representation modules on a Movidius NCS2 device for acceleration, and build another face alignment module on a Raspberry Pi 3B embedded system. Experimental results show that our optimized face recognition has successfully raised its processing speed for over 60 times, and is able to perform a realtime face recognition at 7.031 FPS with a high accuracy of 93%. Moreover, the whole computing system has a low consumption of 6.7W, 17 times lower than similar solutions using CPU and GPU.

Face detection and tracking using edge orientation information An important topic in face recognition as well as in video coding or multimodal human machine interfaces is the automatic detection of faces or headandshoulder regions in visual scenes. The algorithms therefore should be computationally fast enough to allow an online detection and parallel processing of the detected objects. In this paper we describe our ongoing work on face detection using an approach that models the face appearance by edge orientation information. We will show that edge orientation is a powerful local image feature to describe objects like faces or body parts. We will present a simple and efficient method for template matching and object modeling based solely on edge orientation information. We also show how to obtain an optimal face model in the edge orientation domain from a set of training images. Unlike many approaches that model the grey level appearance of the face our approach is computationally very fast. It takes less than 0.1 seconds on a Pentium II 500MHz for a 190x140 image to be processed using a multiresolution search with three resolution levels. We demonstrate the capability of our detection method on an image database of 7000 images taken from more than 200 different people. The variations in head size, lighting and background are considerable. The obtained detection rate is more than 97% on that database. We have also extended the algorithm for face tracking. The tracking capabilities are shown using results from a real  time implementation of the proposed algorithm.