FACE RECOGNITION APPROACH BY USING DLIB AND K-NN

Abstract

The face serves as a unique topographical map that reflects an individual's distinct features. Face recognition has gained prominence as a popular biometric method, especially in security control applications. In this study, we present a system developed using a Haar cascade classifier and Hog-based Dlib face detector for human face detection. Face features are extracted with the Dlib deep metric learning library, and classification is performed using the k-NN algorithm. The system underwent testing on benchmark data within the framework of an exam access control system. The system demonstrated an accuracy of up to 90% in the Orl_Face dataset. The measurement results were compared with other face recognition systems for validation. Beyond accuracy assessments, the proposed system was also compared with similar training tools, fostering a comprehensive discussion on its performance and capabilities.

Keywords

• Face recognition
• Dlib
• Hog
• k-NN
• Exam admission control

References

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