Performance Comparison of Face Detection and Recognition Algorithms

Recognition of faces is a fundamental cognitive ability that forms an important basis for our social interactions. This paper aims to optimize the existing face recognition system by comparing the results of different algorithms. To achieve this goal, I have analyzed state-of-the-algorithms in both face detection and face recognition. The research for algorithms goes through the analysis of recent benchmarks, two of which (i. e. WIDER FACE [1] and MegaFace [2]) are also used for evaluating those algorithms. The results on these benchmarks allows to determine which algorithms perform better, that is to say SSH [3] for detection and both Dlib-R [4] and ArcFace [5] for recognition. All the tests are performed with algorithm efficiency in mind. And computation time measurements show that the best techniques tend to work slower but that they can achieve practical execution times.