scholarly journals Implementasi Convolutional Neural Network untuk Facial Recognition

2021 ◽  
Vol 20 (2) ◽  
pp. 66-79
Author(s):  
Dhanny Setiawan ◽  
Andikha Dwi Putra ◽  
Kezia Stefani ◽  
Jenisa Felisa
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Facial Recognition ◽  
Haar Cascade

Facial recognition merupakan salah satu teknik biometrik. Teknik yang dapat disebut juga pengenalan wajah ini telah menjadi topik yang cukup diminati untuk diteliti. Pada peneitian ini dilakukan proses pengenalan wajah dengan menggunakan metode CNN (Convolutional Neural Network). Penelitian ini memiliki tujuan untuk mengimplementasikan metode CNN ke dalam pengenalan wajah dengan menggunakan library Tensorflow. Metode ini digunakan karena proses pembelajaran dilakukan dengan mendalam (deep learning). Metode CNN yang digunakan memiliki beberapa lapisan pada proses training yang dilakukan, yaitu lapisan Conv2D, MaxPooling2d, Flatten, dan Dense. Face recognition yang dihasilkan terdapat pendeteksi wajah menggunakan Haar Cascade dengan bantuan library Opencv di dalamnya. Jumlah dataset juga diketahui dapat mempengaruhi hasil pengenalan dan proses pengenalan wajah dengan CNN juga memerlukan dataset yang besar. Adapun jumlah citra wajah yang digunakan dalam penelitian ini sebanyak 90.000 gambar wajah yang berasal dari 36 himpunan gambar dan menghasilkan tingkat akurasi sebesar 97%.

scholarly journals Identifying Ethnics of People through Face Recognition: A Deep CNN Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Ahmed Jawad A. AlBdairi ◽  
Zhu Xiao ◽  
Mohammed Alghaili
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Research Community ◽  
Facial Features ◽  
New Model ◽  
Image Dataset ◽  
Deep Cnn

The interest in face recognition studies has grown rapidly in the last decade. One of the most important problems in face recognition is the identification of ethnics of people. In this study, a new deep learning convolutional neural network is designed to create a new model that can recognize the ethnics of people through their facial features. The new dataset for ethnics of people consists of 3141 images collected from three different nationalities. To the best of our knowledge, this is the first image dataset collected for the ethnics of people and that dataset will be available for the research community. The new model was compared with two state-of-the-art models, VGG and Inception V3, and the validation accuracy was calculated for each convolutional neural network. The generated models have been tested through several images of people, and the results show that the best performance was achieved by our model with a verification accuracy of 96.9%.

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

scholarly journals Face Recognition with CNN and Inception Deep Learning Models

2019 ◽  
Vol 8 (3) ◽  
pp. 1932-1938
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Training Image ◽  
Learning Models ◽  
Training Images ◽  
Testing Data ◽  
Facial Images

In this work, deep learning methods are used to classify the facial images. ORL Database is used for the purpose of training the models and for testing. Three kinds of models are developed and their performances are measured. Convolutional Neural Networks (CNN), Convolutional Neural Network Based Inception Model with single training image per class (CNN-INC) and Convolutional Neural Network Based Inception Model with several training images per class (CNN-INC-MEAN) are developed. The ORL database has ten facial images for each person. Five images are used for training purpose and remaining 5 images are used for testing. The five images for the training are chosen randomly so that two sets of training and testing data is generated. The models are trained and tested on the two sets that are drawn from the same population. The results are presented for accuracy of face recognition

scholarly journals ANÁLISE DE MÉTODOS DE DETECÇÃO E RECONHECIMENTO DE FACES UTILIZANDO VISÃO COMPUTACIONAL E ALGORITMOS DE APRENDIZADO DE MÁQUINA

2021 ◽  
Vol 13 (2) ◽  
pp. 01-11
Author(s):  
Lucas José da Costa ◽  
Thiago Luz de Sousa ◽  
Francisco Assis da Silva ◽  
Leandro Luiz de Almeida ◽  
Danillo Roberto Pereira ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Comparative Analysis ◽  
Execution Time ◽  
Digital Images ◽  
Facial Recognition ◽  
Detection Algorithm ◽  
Ambient Lighting ◽  
The Face ◽  
Haar Cascade

The advancement in technology in recent decades has provided many facilities for humanity in various applications, and facial recognition technology is one of them. There are several problemsto be solved to perform face recognition from digital images, such as varying ambient lighting, changing the face physical characteristics and resolution of the images used. This work aimed to perform a comparative analysis between some of thedetection and facial recognition methods, as well as their execution time. We use the Eigenface, Fisherface and LBPH facial recognition algorithms in conjunction with the Haar Cascade facedetection algorithm, all from the OpenCV library. We also explored the use of CNN neural network for facial recognition in conjunction with the HOG facial detection algorithm, these from the Dlib library. The work aimed, besides analyzing the algorithms in relation to hit rates, factors such as reliability and execution time were also considered

scholarly journals Face Recognition Untuk Akses Pegawai Bank Menggunakan Deep Learning Dengan Metode CNN

2020 ◽  
Vol 6 (1) ◽  
pp. 55-63 ◽  
Author(s):  
Muhammad Arsal ◽  
Bheta Agus Wardijono ◽  
Dina Anggraini
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Total Data

Perkembangan teknologi berkembang dengan sangat cepat, sehingga memberikan banyak manfaat khususnya dalam bidang Teknologi Informasi. Permintaan layanan dengan penggunaan teknologi semakin banyak dibutuhkan oleh perusahaan. Salah satu perusahaan yang membutuhkannya adalah perusahaan perbankan. Perkembangan teknologi memudahkan perusahaan untuk bisa menyelesaikan masalah, salah satunya masalah dalam sistem keamanan. Sistem kemanan dibutuhkan dalam setiap perusahaan dalam segala aspek. Sistem keamanan untuk pintu akses pegawai merupakan permintaan sebuah perusahaan bank yang dibuat dalam penelitian ini. Pada penelitian ini, dilakukan sistem keamanan pintu akses pegawai bank dengan menggunakan face recognition. Teknologi Face Recognition menggunakan pembahasan Deep Learning. Pembuatan aplikasi ini menggunakan algoritma Convolutional Neural Network (CNN). Bahasa pemrograman yang digunakan dalam pembuatan program ini yaitu python.  Proses pembuatan aplikasi ini dengan tahapan pembuatan Face Recognition yaitu akuisisi gambar, preprocessing, ektraksi, klasifikasi, dan identifikasi data gambar. Penelitian ini berhasil menggunakan Face Recognition oleh 5 orang dataset wajah pegawai bank yang terdiri dari 70 data wajah pada masing-masing orang. Sehingga total data wajah yang digunakan 350 data wajah. Dataset tersebut dipisahkan menjadi 3 tahapan data yaitu data train, data validasi, dan data uji. Hasil dari pengujian ketiga dataset tersebut berhasil mengidentifikasi wajah yang ditangkap oleh kamera dengan persentase keakuratan 95%. Program pada penelitian ini berhasil digunakan bank untuk pintu akses ruangan perkantoran oleh pegawai bank.

scholarly journals Celebrity Face Recognition using Deep Learning

2018 ◽  
Vol 12 (2) ◽  
pp. 476 ◽  
Author(s):  
Nur Ateqah Binti Mat Kasim ◽  
Nur Hidayah Binti Abd Rahman ◽  
Zaidah Ibrahim ◽  
Nur Nabilah Abu Mangshor
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Facial Expressions ◽  
Experimental Results ◽  
Learning Technique ◽  
Excellent Accuracy ◽  
Better Than

Face recognition is one of the well studied problems by researchers in computer visions.  Among the challenges of this task are the occurrence of different facial expressions like happy or sad, and different views of the images such as front and side views.  This paper experiments a publicly available dataset that consists of 200,000 images of celebrity faces. Deep Learning technique is gaining its popularity in computer vision and this paper applies this technique for face recognition problem.  One of the techniques under deep learning is Convolutional Neural Network (CNN).  There is also pre-trained CNN models that are AlexNet and GoogLeNet, which produce excellent accuracy results.  The experimental results indicate that AlexNet is better than basic CNN and GoogLeNet for face recognition.

scholarly journals Face recognition based on full convolutional neural network based on transfer learning model

2021 ◽  
pp. 28-28
Author(s):  
Zhongkui Fan ◽  
Ye-Peng Guan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Test Accuracy ◽  
Great Success ◽  
Data Set ◽  
Face Features ◽  
The Face

Deep learning has achieved a great success in face recognition (FR), however, little work has been done to apply deep learning for face photo-sketch recognition. This paper proposes an adaptive scale local binary pattern extraction method for optical face features. The extracted features are classified by Gaussian process. The most authoritative optical face test set LFW is used to train the trained model. Test, the test accuracy is 98.7%. Finally, the face features extracted by this method and the face features extracted from the convolutional neural network method are adapted to sketch faces through transfer learning, and the results of the adaptation are compared and analyzed. Finally, the paper tested the open-source sketch face data set CUHK Face Sketch database(CUFS) using the multimedia experiment of the Chinese University of Hong Kong. The test result was 97.4%. The result was compared with the test results of traditional sketch face recognition methods. It was found that the method recognized High efficiency, it is worth promoting.

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

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