Localized Deep-CNN Structure for Face Recognition

2018 ◽  
Author(s):  
Adil Al-Azzawi ◽  
Jade Hind ◽  
Jianlin Cheng
Keyword(s):  
Face Recognition ◽  
Deep Cnn

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%.

How Image Degradations Affect Deep CNN-Based Face Recognition?

2016 ◽  
Author(s):  
Samil Karahan ◽  
Merve Kilinc Yildirum ◽  
Kadir Kirtac ◽  
Ferhat Sukru Rende ◽  
Gultekin Butun ◽  
...  
Keyword(s):  
Face Recognition ◽  
Deep Cnn

Deep CNN-Based Face Recognition

2020 ◽  
pp. 1-9
Author(s):  
Ankan Bansal ◽  
Rajeev Ranjan ◽  
Carlos D. Castillo ◽  
Rama Chellappa
Keyword(s):  
Face Recognition ◽  
Deep Cnn

scholarly journals A Modified CNN-Based Face Recognition System

2021 ◽  
Vol 12 (2) ◽  
pp. 1-20
Author(s):  
Jayanthi Raghavan ◽  
Majid Ahmadi
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Softmax Classifier ◽  
Face Recognition System ◽  
Proposed Model ◽  
The Face ◽  
Batch Sizes ◽  
Deep Cnn ◽  
Fully Connected ◽  
Improved Accuracy

In this work, deep CNN based model have been suggested for face recognition. CNN is employed to extract unique facial features and softmax classifier is applied to classify facial images in a fully connected layer of CNN. The experiments conducted in Extended YALE B and FERET databases for smaller batch sizes and low value of learning rate, showed that the proposed model has improved the face recognition accuracy. Accuracy rates of up to 96.2% is achieved using the proposed model in Extended Yale B database. To improve the accuracy rate further, preprocessing techniques like SQI, HE, LTISN, GIC and DoG are applied to the CNN model. After the application of preprocessing techniques, the improved accuracy of 99.8% is achieved with deep CNN model for the YALE B Extended Database. In FERET Database with frontal face, before the application of preprocessing techniques, CNN model yields the maximum accuracy of 71.4%. After applying the above-mentioned preprocessing techniques, the accuracy is improved to 76.3%.

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