Face image recognition based on convolutional neural network

Lou, Guangxin; Shi, Hongzhen

doi:10.23919/jcc.2020.02.010

Cited by 113 publications

(52 citation statements)

References 11 publications

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“…Our main motivation was to verify the experimental effectiveness of recognizing artefacts with different levels of visibility on satellite images. As a reference classifier we have chosen the LeNet type convolutional neural network [46,47]-the general scheme is shown in Figure 4. The exact architecture of the network used can be seen in Section 4.1 and Figure 5.…”

Section: Motivation and Methodologymentioning

confidence: 99%

Deep Learning for RFI Artifact Recognition in Sentinel-1 Data

2020

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Beyond the variety of unwanted disruptions that appear quite frequently in synthetic aperture radar (SAR) measurements, radio-frequency interference (RFI) is one of the most challenging issues due to its various forms and sources. Unfortunately, over the years, this problem has grown worse. RFI artifacts not only hinder processing of SAR data, but also play a significant role when it comes to the quality, reliability, and accuracy of the final outcomes. To address this issue, a robust, effective, and—importantly—easy-to-implement method for identifying RFI-affected images was developed. The main aim of the proposed solution is the support of the automatic permanent scatters in SAR (PSInSAR) processing workflow through the exclusion of contaminated SAR data that could lead to misinterpretation of the calculation results. The approach presented in this paper for the purpose of recognition of these specific artifacts is based on deep learning. Considering different levels of image damage, we used three variants of a LeNet-type convolutional neural network. The results show the high efficiency of our model used directly on sample data.

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Section: Motivation and Methodologymentioning

confidence: 99%

Deep Learning for RFI Artifact Recognition in Sentinel-1 Data

2020

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“…The VGG16 based multi-level information fusion model [25]. On fully connected neural network, enhancement is proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Convolutional Neural Networks (CNNs) And VGG on Real Time Face Recognition System

Dar¹,

Palanivel²

2021

Adv. sci. technol. eng. syst. j.

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Face Recognition (FR) is considered as a heavily studied topic in computer vision field. The capability to automatically identify and authenticate human's faces using real-time images is an important aspect in surveillance, security, and other related domains. There are separate applications that help in identifying individuals at specific locations which help in detecting intruders. The real-time recognition is mandatory for surveillance purposes. A number of machine learning methods along with classifiers are used for the recognition of faces. Existing Machine Learning (ML) methods are failed to achieve optimal performance due to their inability to accurately extract the features from the face image, and enhancing system's recognition accuracy system becomes very difficult task. Majority of designed FR systems has two major steps: extraction of feature and classifier. Accurate FR system is still a challenge, primarily due to the higher computational time and separate feature extraction. In general, for various applications using images, deep learning algorithms are mostly recommended for solving these problems because it performs combined feature extraction and classification task. Deep learning algorithm reduces the computation time and enhances the recognition accuracy because of automatic extraction of feature. The major novelty of the work is to design a new VGG-16 with Transfer Learning algorithm for face recognition by varying active layers with three levels (3, 4, and 7). It also designs the Convolutional Neural Network (CNN) for FR system. The proposed work introduced a new Real Time Face Recognition (RTFR) system. The process is broken into three major steps: (1) database collection, (2) FR to identify particular persons and (3) Performance evaluation. For the first step, the system collects 1056 faces in real time for 24 persons using a camera with resolution of 112*92. Second step, efficient RTFR algorithm is then used to recognize faces with a known database. Here two different deep learning algorithms such as CNN and VGG-16 with Transfer Learning are introduced for RTFR system. This proposed system is implemented using Keras. Thirdly the performance of these two classifiers is measured using of precision, recall, F1-score, accuracy and k-fold cross validation. From results it concludes that proposed algorithm produces higher accuracy results of 99.37%, whereas the other existing classifiers such as VGG3, VGG7, and CNN gives the accuracy results of 75.71%, 96.53%, and 69.09% values respectively.

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“…As a reference classifier defined in the following section, we used the Convolutional Neural Network (CNN) [59,60].…”

Section: Nearest Neighbor Structure Filteringmentioning

confidence: 99%

RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

Chojka

Artiemjew

Rapiński

2020

Sensors

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Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used.

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Face image recognition based on convolutional neural network

Cited by 113 publications

References 11 publications

Deep Learning for RFI Artifact Recognition in Sentinel-1 Data

Deep Learning for RFI Artifact Recognition in Sentinel-1 Data

Performance Evaluation of Convolutional Neural Networks (CNNs) And VGG on Real Time Face Recognition System

RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

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