Face Recognition with CNN and Inception Deep Learning Models
Lakshmi Patil1, V.D. Mytri2

1Lakshmi Patil1, Research ScholarDept. of ECE, Appa Institute Of Engineering and Technology. 
2V D Mytri2, Professor, Dept of ECE, Appa Institute Of Engineering and Technology.

Manuscript received on 11 August 2019. | Revised Manuscript received on 21 August 2019. | Manuscript published on 30 September 2019. | PP: 1932-38 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4476098319/19©BEIESP | DOI: 10.35940/ijrte.C4476.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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.
Keywords— Convolutional Neural Networks, Inception Models, Face Recognition.

Scope of the Article:
Machine/ Deep Learning with IoT & IoE