Open AccessFace recognition system Using Deep Neural Network with Convolutional Neural Networks
Author(s) -
Erick Fernando,
Denny Andwiyan,
Dina Fitria Murad,
Derist Touriano,
Muhamad Irsan
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1235/1/012004
Subject(s) - computer science , artificial intelligence , convolutional neural network , facial recognition system , pattern recognition (psychology) , face (sociological concept) , biometrics , artificial neural network , deep learning , feature (linguistics) , feature extraction , computer vision , social science , linguistics , philosophy , sociology
Face recognition has long been a hot topic and challenging research point in areas such as image processing, pattern recognition, and machine vision. The face is a biometric feature with the intrinsic nature of a human. So that the face has self-stability, deep individual differences and can be an ideal basis for verification of an identity. In this research use, Deep Learning Network method uses to perform detection or face recognition. In this study, we present a framework that can be used to detect faces. This research is also able to present a DNN model that is used to study data sources from the data stream in sequence. The most important part of this study is able to adjust the capacity of the model from the simple one. This research uses experimental design method. The first step is a collection of face image data. Then the architecture design starts from the determination of the depth of the network, layout layers, and the selection of layer types that will be used to get the model based on input dataset and label name index.