Analysis of Face Recognition Algorithm: Dlib and OpenCV


Suwarno Suwarno(1*), Kevin Kevin(2),


(1) Universitas Internasional Batam
(2) 
(*) Corresponding Author

Abstract


In face recognition there are two commonly used open-source libraries namely Dlib and OpenCV. Analysis of facial recognition algorithms is needed as reference for software developers who want to implement facial recognition features into an application program. From Dlib algorithm to be analyzed is CNN and HoG, from OpenCV algorithm is DNN and HAAR Cascades. These four algorithms are analyzed in terms of speed and accuracy. The same image dataset will be used to test, along with some actual images to get a more general analysis of how algorithm will appear in real life scenarios. The programming language used for face recognition algorithms is Python. The image dataset will come from LFW (Labeled Faces in the Wild), and AT&T, both of which are available and ready to be downloaded from the internet. Pictures of people around the UIB (Batam International University) is used for actual images dataset. HoG algorithm is fastest in speed test (0.011 seconds / image), but the accuracy rate is lower (FRR = 27.27%, FAR = 0%). DNN algorithm is the highest in level of accuracy (FRR = 11.69%, FAR = 2.6%) but the lowest speed (0.119 seconds / picture). There is no best algorithm, each algorithm has advantages and disadvantages.
Keywords: Python, Face Recognition, Analysis, Speed, Accuracy.


Keywords


Python, Face Recognition, Analysis, Speed, Accuracy

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DOI: https://doi.org/10.31289/jite.v4i1.3865

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