scholarly journals PSI-CNN: A Pyramid-Based Scale-Invariant CNN Architecture for Face Recognition Robust to Various Image Resolutions

2018 ◽  
Vol 8 (9) ◽  
pp. 1561 ◽  
Author(s):  
Gi Nam ◽  
Heeseung Choi ◽  
Junghyun Cho ◽  
Ig-Jae Kim
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Research Area ◽  
Feature Maps ◽  
Matching Accuracy ◽  
Scale Invariant ◽  
Face Model ◽  
Multiple Image ◽  
Face Matching ◽  
Independent Information

Face recognition is one research area that has benefited from the recent popularity of deep learning, namely the convolutional neural network (CNN) model. Nevertheless, the recognition performance is still compromised by the model’s dependency on the scale of input images and the limited number of feature maps in each layer of the network. To circumvent these issues, we propose PSI-CNN, a generic pyramid-based scale-invariant CNN architecture which additionally extracts untrained feature maps across multiple image resolutions, thereby allowing the network to learn scale-independent information and improving the recognition performance on low resolution images. Experimental results on the LFW dataset and our own CCTV database show PSI-CNN consistently outperforming the widely-adopted VGG face model in terms of face matching accuracy.

scholarly journals Modified SIFT Descriptors for Face Recognition under Different Emotions

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Nirvair Neeru ◽  
Lakhwinder Kaur
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
User Interaction ◽  
Recognition Algorithm ◽  
Scale Invariant ◽  
Distance Ratio ◽  
Japanese Female ◽  
Database Size ◽  
Fully Automatic ◽  
Scale Invariant Feature

The main goal of this work is to develop a fully automatic face recognition algorithm. Scale Invariant Feature Transform (SIFT) has sparingly been used in face recognition. In this paper, a Modified SIFT (MSIFT) approach has been proposed to enhance the recognition performance of SIFT. In this paper, the work is done in three steps. First, the smoothing of the image has been done using DWT. Second, the computational complexity of SIFT in descriptor calculation is reduced by subtracting average from each descriptor instead of normalization. Third, the algorithm is made automatic by using Coefficient of Correlation (CoC) instead of using the distance ratio (which requires user interaction). The main achievement of this method is reduced database size, as it requires only neutral images to store instead of all the expressions of the same face image. The experiments are performed on the Japanese Female Facial Expression (JAFFE) database, which indicates that the proposed approach achieves better performance than SIFT based methods. In addition, it shows robustness against various facial expressions.

scholarly journals Efficient Masked Face Recognition Method during the COVID-19 Pandemic

2020 ◽  
Author(s):  
Walid Hariri
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Efficient Solutions ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Face Region ◽  
The Face ◽  
To Come ◽  
Primary Focus ◽  
Fully Connected

Abstract The COVID-19 is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on discard masked region and deep learning-based features in order to address the problem of the masked face recognition process. The first step is to discard the masked face region. Next, we apply pre-trained deep Convolutional neural networks (CNN) to extract the best features from the obtained regions (mostly eyes and forehead regions). Finally, the Bag-of-features paradigm is applied on the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance.

scholarly journals Efficient Masked Face Recognition Method during the COVID-19 Pandemic

2020 ◽  
Author(s):  
Walid Hariri
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Efficient Solutions ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Face Region ◽  
The Face ◽  
To Come ◽  
Primary Focus ◽  
Fully Connected

Abstract The COVID-19 is an unparalleled crisis leading to huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes the face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on discard masked region and deep learning based features in order to address the problem of masked face recognition process. The first step is to discard the masked face region. Next, we apply a pre-trained deep Convolutional neural networks (CNN) to extract the best features from the obtained regions (mostly eyes and forehead regions). Finally, the Bag-of-features paradigm is applied on the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, MLP is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance.

scholarly journals Efficient Masked Face Recognition Method during the COVID-19 Pandemic

2020 ◽  
Author(s):  
Walid Hariri
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Efficient Solutions ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Face Region ◽  
The Face ◽  
To Come ◽  
Primary Focus ◽  
Fully Connected

Abstract The COVID-19 is an unparalleled crisis leading to huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes the face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on discard masked region and deep learning based features in order to address the problem of masked face recognition process. The first step is to discard the masked face region. Next, we apply a pre-trained deep Convolutional neural networks (CNN) to extract the best features from the obtained regions (mostly eyes and forehead regions). Finally, the Bag-of-features paradigm is applied on the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance.

Face Perception in Face Transplant Patients

2019 ◽  
Vol 35 (05) ◽  
pp. 525-533
Author(s):  
Evrim Gülbetekin ◽  
Seda Bayraktar ◽  
Özlenen Özkan ◽  
Hilmi Uysal ◽  
Ömer Özkan
Keyword(s):  
Face Recognition ◽  
Object Recognition ◽  
Face Processing ◽  
Recognition Performance ◽  
Discrimination Performance ◽  
Object Discrimination ◽  
Face Transplantation ◽  
Transplant Patients ◽  
Facial Surgery ◽  
The Face

AbstractThe authors tested face discrimination, face recognition, object discrimination, and object recognition in two face transplantation patients (FTPs) who had facial injury since infancy, a patient who had a facial surgery due to a recent wound, and two control subjects. In Experiment 1, the authors showed them original faces and morphed forms of those faces and asked them to rate the similarity between the two. In Experiment 2, they showed old, new, and implicit faces and asked whether they recognized them or not. In Experiment 3, they showed them original objects and morphed forms of those objects and asked them to rate the similarity between the two. In Experiment 4, they showed old, new, and implicit objects and asked whether they recognized them or not. Object discrimination and object recognition performance did not differ between the FTPs and the controls. However, the face discrimination performance of FTP2 and face recognition performance of the FTP1 were poorer than that of the controls were. Therefore, the authors concluded that the structure of the face might affect face processing.

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