Face modeling process based on Dlib

Ren, Xiujuan; Ding, Junhang; Sun, Jing-Chao; Sui, Qingmei

doi:10.1109/cac.2017.8243093

Cited by 6 publications

(1 citation statement)

References 5 publications

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“…Moreover, we utilize traditional classifiers as Knn [26], SVM [22], Random Forest [23], Decision Tree [25] and Naïve Bayes [24] to examine the suitable classifier for the extracted features. Finally, some applications are deployed using face recognition ( [3], [4], [12], [15]) with special task such as recognition actors of a film in [12] or face modelling in [15]. In this paper, we would like present an intelligent door system that face recognition results is decoding role.…”

Section: Introductionmentioning

confidence: 99%

Realtime Face Recognition for Intelligent Door System

2020

IJETER

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This paper focus on in-deep analysis of the majority of both hand-craft based and CNN-based that proposed in states-of-the-art for face detection and face recognition method. This issue has been researched for recent decades as it enjoys many practical applications such as entertainments, security, tracking, human-machine interaction and so on. However, this problem has been faced to many challenges such as complex background, illumination, face pose, states of face, fake face. Which lead to high time cost, and low accuracy system. In recent times, thanks to the cutting-edge technique that give impressive performance on both hand craft-based and deep learning-based method. There have been improvement in detecting face exploiting feature vectors. Therefore, in this research, we comparing face detection model such as Haar Cascade[6], Dlib [19] and YOLO [27]. Then, we consider and compare affects of methods when extract feature vectors. Besides, we try to examine on various classifications to obtain the suitable system. Evaluation results confirm that the best accuracy rate achieves at 97.83%. The proposed method suggests a feasible solution addressing technical issues in using hand craft-based face recognition. Moreover, we deploy a realtime intelligent door system that utilize face recognition results to automatically control door.

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