Face Recognition with CNN and Inception Deep Learning Models

Abstract

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

Analisis Pemilihan Optimizer dalam Arsitektur Convolution Neural Network VGG16 dan Inception untuk Sistem Pengenalan Wajah

“…Kemudian dilakukan proses konvolusi dengan melibatkan pendeteksi fitur, dikenal sebagai kernel atau filter. Keluaran hasil konvolusi disebut sebagai feature map [9]. Ukuran filter dapat ditentukan oleh peneliti tergantung kepada fitur yang ingin dipelajari dari citra masukan.…”

A Review: Face Recognition Techniques using Deep Learning

“…Used CNN architecture is evaluating their method on the MORPH database with result accuracy was 93.6%. Lakshmi [22] used The ORL database will be used to train and test the models. Three different types of models are created, and their performance is evaluated.…”