Comparisons of facial recognition algorithms through a case study application

Abstract

Computer visions and its applications have become important in the contemporary life. Hence, researches on facial and object recognitions have become increasingly important both from academicians and practitioners. Smart gadgets such as smart phones are nowadays capable of high processing power, memory capacity, along with high resolutions camera. Furthermore, the connectivity bandwidth and the speed of the interaction have significantly impacted the popularity of mobile object recognition's applications. These developments in addition to computer vision's algorithms advancement has transferred object's recognitions from desktop environments to mobile world. The aim of this paper to reveal the efficiency and accuracy of the existing open source facial recognition algorithms in real-life setting. We use the following popular open source algorithms for efficiency evaluations: Eigenfaces, Fisherfaces, Local Binary Pattern Histogram, the deep convolutional neural network algorithm and Open-Face. The evaluations of the test cases indicate that among the compared facial recognition algorithms the OpenFace algorithm has the highest accuracy to identify faces. The findings of this study help the practitioner on their decision of the algorithm selections and the academician on how to improve the accuracy of the current algorithms even further.