Abstract
<p class="affiliations"><strong>Abstract— </strong>Computer visions and their applications have become important in contemporary life. Hence, researches on facial and object recognition have become increasingly important both from academicians and practitioners. Smart gadgets such as smartphones 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 applications. These developments in addition to computer vision’s algorithms advancement have transferred object’s recognitions from desktop environments to the mobile world. The aim of this paper to reveal the efficiency and accuracy of the existing open-source facial recognition algorithms in real-life settings. We use the following popular open-source algorithms for efficiency evaluations: Eigenfaces, Fisherfaces, Local Binary Pattern Histogram, the deep convolutional neural network algorithm, and OpenFace. 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.</p>
Highlights
We deal with photos and images regularly through our smart phones
What are the differences between these two and how accurate they are in detecting a face when compared to each other? In the first part of this study, we provide an overview of the logic behind different facial recognition algorithms
The performance of OpenFace dropped to 80% with 15 subjects. These results demonstrate that accuracy depends on number of training and testing samples, and on the specific samples
Summary
We deal with photos and images regularly through our smart phones. These devices and images are seen and taken everywhere, e.g., in streets, in supermarkets, and in many other public locations. We use cameras for various purposes such as security [1], health [2]. Athletics use cameras such as GoPro’s [3] for recording extreme sports and share the excitement with others either as live stream or offline. The use of camera has extended to the overcome some of the human constrain such as helping the color blind people [4]. In addition to the hand-held cameras for offline and online detection ,e.g. [5], the usage of drone based cameras has increased [6]. Drones are used to take and compare images especially for areas that are not easy to explore
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