Face image manipulation detection based on a convolutional neural network

2019 ◽  
Vol 129 ◽  
pp. 156-168 ◽  
Author(s):  
L. Minh Dang ◽  
Syed Ibrahim Hassan ◽  
Suhyeon Im ◽  
Hyeonjoon Moon
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Face Image ◽  
Image Manipulation ◽  
Manipulation Detection

scholarly journals Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Er-Yang Huan ◽  
Gui-Hua Wen ◽  
Shi-Jun Zhang ◽  
Dan-Yang Li ◽  
Yang Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Chinese Medicine ◽  
Convolutional Neural Network ◽  
Recognition Algorithm ◽  
Face Image ◽  
Deep Convolutional Neural Networks ◽  
Softmax Classifier ◽  
Core Content ◽  
Body Constitution ◽  
Proposed Model

Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.

scholarly journals Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender Berdasarkan Citra Wajah

2019 ◽  
Vol 18 (2) ◽  
pp. 211-221
Author(s):  
Bambang Krismono Triwijoyo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Real World ◽  
Face Image ◽  
Gender Classification ◽  
Learning Method ◽  
Image Transfer ◽  
Real World Applications ◽  
The Face

The face is a challenging object to be recognized and analyzed automatically by a computer in many interesting applications such as facial gender classification. The large visual variations of faces, such as occlusions, pose changes, and extreme lightings, impose great challenge for these tasks in real world applications. This paper explained the fast transfer learning representations through use of convolutional neural network (CNN) model for gender classification from face image. Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. The experimental results showed that the transfer learning method have faster and higher accuracy than CNN network without transfer learning.

scholarly journals Face Image Set Recognition Based On Improved HOG-NMF and Convolutional Neural Networks

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Lixiu Hao ◽  
Weiwei Yu
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Adverse Effects ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Weight Coefficient ◽  
Face Image ◽  
Segmented Images ◽  
The Face ◽  
Image Set

Objective Face recognition can be affected by unfavorable factors such as illumination, posture and expression, but the face image set is a collection of people’s various angles, different illuminations and even different expressions, which can effectively reduce these adverse effects and get higher face recognition rate. In order to make the face image set have higher recognition rate, a new method of combining face image set recognition is proposed, which combines an improved Histogram of Oriented Gradient (HOG) feature and Convolutional Neural Network (CNN). Method The method firstly segments the face images to be identified and performs HOG to extract features of the segmented images. Secondly, calculate the information entropy contained in each block as a weight coefficient of each block to form a new HOG features, and non-negative matrix factorization (NMF) is applied to reduce HOG features. Then the reduced-dimensional HOG features are modeled as image sets which keep your face details as much as possible. Finally, the modeled image sets are classified by using a convolutional neural network. Result The experimental results show that compared with the simple CNN method and the HOG-CNN method, the recognition rate of the method on the CMU PIE face set is increased by about 4%~10%. Conclusion The method proposed in this paper has more details of the face, overcomes the adverse effects, and improves the accuracy.

scholarly journals Effects of image compression on face image manipulation detection: A case study on facial retouching

2021 ◽  
Author(s):  
Christian Rathgeb ◽  
Kevin Bernardo ◽  
Nathania E. Haryanto ◽  
Christoph Busch
Keyword(s):  
Image Compression ◽  
Face Image ◽  
Image Manipulation ◽  
Manipulation Detection
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