scholarly journals Deepfake Video Forensics based on Transfer Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 5069-5073
Keyword(s):  
Neural Network ◽  
Good Accuracy ◽  
Visual Media ◽  
Video Frame ◽  
Classification Models ◽  
Video Forensics ◽  
Major Threat ◽  
Video Frames ◽  
The Neural Network ◽  
The Face

Deeplearning has been used to solve complex problems in various domains. As it advances, it also creates applications which become a major threat to our privacy, security and even to our Democracy. Such an application which is being developed recently is the "Deepfake". Deepfake models can create fake images and videos that humans cannot differentiate them from the genuine ones. Therefore, the counter application to automatically detect and analyze the digital visual media is necessary in today world. This paper details retraining the image classification models to apprehend the features from each deepfake video frames. After feeding different sets of deepfake clips of video fringes through a pretrained layer of bottleneck in the neural network is made for every video frame, already stated layer contains condense data for all images and exposes artificial manipulations in Deepfake videos. When checking Deepfake videos, this technique received more than 87 per cent accuracy. This technique has been tested on the Face Forensics dataset and obtained good accuracy in detection.

Fractional Krill–Lion Algorithm Based Actor Critic Neural Network for Face Recognition in Real Time Surveillance Videos

2019 ◽  
Vol 18 (02) ◽  
pp. 1950011
Author(s):  
Deepak S. Dharrao ◽  
Nilesh J. Uke
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Super Resolution ◽  
Directional Pattern ◽  
Video Frame ◽  
Feature Descriptor ◽  
Video Frames ◽  
The Face ◽  
Critic Neural Network ◽  
Face Part

Face recognition from low quality videos is one of the major challenges prevailing in video surveillance system. Several works have been contributed towards the face recognition, but have suffered due to the fact that the low quality videos have face part with low resolution. Also, using traditional feature extraction schemes make the recognition process to be tough. This paper introduces a novel feature descriptor and classification scheme for recognizing the face from low quality videos. Here, the video frames are sequentially provided to the Viola–Jones algorithm for detecting the face part, and the quality of the face part is improved by applying bi-cubic interpolation based super resolution scheme. Now, the features of enhanced video frame are extracted using proposed local direction feature descriptor, namely scattering wavelet-based local directional pattern (SW-LDP). Then, the extracted features are fed as the input to the actor critic neural network, where training is done using the newly developed fractional calculus based krill–lion (fractional KL) algorithm. The proposed fractional KL-ACNN algorithm is experimented using the standard FAMED database. From the analysis, it is evident that the proposed classifier achieved low FAR of 3.89%, and low FRR of 4.04%, and high accuracy value of 95%, respectively.

scholarly journals Convolutional Neural Networks and Geometric Moments to Identify the Bilateral Symmetric Midplane in Facial Skeletons from CT Scans

Biology ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 182
Author(s):  
Rodrigo Dalvit Carvalho da Silva ◽  
Thomas Richard Jenkyn ◽  
Victor Alexander Carranza
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Surgical Planning ◽  
Good Accuracy ◽  
Bilateral Symmetry ◽  
Ct Scans ◽  
Facial Skeleton ◽  
Intact Side ◽  
Geometric Moments ◽  
The Face

In reconstructive craniofacial surgery, the bilateral symmetry of the midplane of the facial skeleton plays an important role in surgical planning. Surgeons can take advantage of the intact side of the face as a template for the malformed side by accurately locating the midplane to assist in the preparation of the surgical procedure. However, despite its importance, the location of the midline is still a subjective procedure. The aim of this study was to present a 3D technique using a convolutional neural network and geometric moments to automatically calculate the craniofacial midline symmetry of the facial skeleton from CT scans. To perform this task, a total of 195 skull images were assessed to validate the proposed technique. In the symmetry planes, the technique was found to be reliable and provided good accuracy. However, further investigations to improve the results of asymmetric images may be carried out.

Adaptive GLOH with PSO-trained NN for the recognition of plastic surgery faces and their types

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Archana Harsing Sable ◽  
Sanjay N. Talbar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Plastic Surgery ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Traditional Learning ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Orientation Histogram ◽  
The Face

Abstract Numerous algorithms have met complexity in recognizing the face, which is invariant to plastic surgery, owing to the texture variations in the skin. Though plastic surgery serves to be a challenging issue in the domain of face recognition, the concerned theme has to be restudied for its hypothetical and experimental perspectives. In this paper, Adaptive Gradient Location and Orientation Histogram (AGLOH)-based feature extraction is proposed to accomplish effective plastic surgery face recognition. The proposed features are extracted from the granular space of the faces. Additionally, the variants of the local binary pattern are also extracted to accompany the AGLOH features. Subsequently, the feature dimensionality is reduced using principal component analysis (PCA) to train the artificial neural network. The paper trains the neural network using particle swarm optimization, despite utilizing the traditional learning algorithms. The experimentation involved 452 plastic surgery faces from blepharoplasty, brow lift, liposhaving, malar augmentation, mentoplasty, otoplasty, rhinoplasty, rhytidectomy and skin peeling. Finally, the proposed AGLOH proves its performance dominance.

scholarly journals Plant Disease Detection and Monitoring Using Artificial Neural Network

2022 ◽  
Vol 10 (01) ◽  
pp. 715-722
Author(s):  
Stella I. Orakwue ◽  
Nkolika O. Nwazor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Crop Production ◽  
Plant Disease ◽  
Disease Detection ◽  
Detection Accuracy ◽  
Major Threat ◽  
The Neural Network ◽  
Artificial Neural ◽  
Hyperspectral Sensor

Fungi have been identified as a major threat to crop production in the world. In this study, methods of improving the performance of plant disease detection and prediction using artificial neural network techniques are presented. The hyperspectral fungi dataset of 21 plant species were collected and trained using backpropagation algorithms of an artificial neural network to improve the conventional hyperspectral sensor. The system was modelled using self-defining equations and universal modelling diagrams and then implemented in the neural network toolbox in Matlab. The system was tested validated and the result showed a fungi detection accuracy of 96.61% and the percentage increment was 19.53%.

scholarly journals Development of an algorithm for object access system automation based on face recognition using a neural network

Connectivity ◽  
2020 ◽  
Vol 145 (3) ◽  
Author(s):  
V. S. Orlenko ◽  
◽  
I. I. Kolosinsʹkyy
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Neural Model ◽  
Large Datasets ◽  
Second Step ◽  
Test Set ◽  
Access System ◽  
The Neural Network ◽  
The Face ◽  
Face Search

The article deals with the technical side of face recognition — the neural network. The advantages of the neural network for identification of the person are substantiated, the stages of comparison of two images are considered. The first step is defined as the face search in the photo. Using several tests, the best neural network was identified, which allowed to effectively obtain a normalized image of a person’s face. The second step is to find the features of the person, for which the comparative analysis is performed. It was this stage that became the main point in this article — 16 sets of tests were carried out, each test set has 12 tests inside. Two large datasets were used for the study to evaluate the effectiveness of the algorithms not only in ideal circumstances but also in the field. The results of the study allowed us to determine the best method and neural model for finding a face and dividing it into parts. It is determined which part of the face the algorithm recognizes best — it will allow making adjustments to the location of the camera.

Development of a Monitoring System for Near Burner Slag Formation

2002 ◽  
Author(s):  
C. K. Tan ◽  
S. J. Wilcox ◽  
J. Ward
Keyword(s):  
Neural Network ◽  
Refractory Material ◽  
Coal Combustion ◽  
Large Range ◽  
Slag Formation ◽  
Ir Radiation ◽  
The Neural Network ◽  
The Face ◽  
Series Of Experiments ◽  
Self Organising Map

A series of experiments on two different coals at a range of burner conditions have been conducted to investigate the behaviour of pf coal combustion on a 150kW pulverised fuel (pf) coal burner with a simulated eyebrow (a growth of slag in the near burner region). The simulation of a burner eyebrow was achieved by inserting an annulus of refractory material immediately in front of the face of the original burner quarl. Results obtained from monitoring the infrared (IR) radiation and sound emitted by the flame were processed into a number of features which were then used to train and test a self organising map neural network. Results obtained from the neural network demonstrated a classification success, never lower than 99.3%, indicate that it is not only possible to detect the presence of an eyebrow by monitoring the flame, but it is also possible to give an indication as to its size, over a reasonably large range of conditions.

scholarly journals Modelo para detectar el uso correcto de mascarillas en tiempo real utilizando redes neuronales convolucionales

2021 ◽  
Vol 9 (17) ◽  
pp. 111-120
Author(s):  
Hugo Andrade Carrera ◽  
Soraya Sinche Maita ◽  
Pablo Hidalgo Lascano
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Face Mask ◽  
Training Process ◽  
Public Places ◽  
The Neural Network ◽  
Great Performance ◽  
The Family ◽  
The Face ◽  
Mandatory Use

Since Covid-19 appeared, the world has entered into a new stage, in which everybody is trying to mitigate the effects of the virus. The mandatory use of face masks in public places and when maintaining contact with people outside the family circle is one of mandatory measures that many countries have implemented, such as Ecuador, thus, the purpose of this article is to develop a convolutional neural network model using TensorFlow based on MobileNetV2, that allows to perform mask detection in real time video with the key feature of determining if the person is using the face mask properly or if it is not wearing a mask, in order to use the model with OpenCV and a pretrained neural network that detects faces. In addition, the performance metrics of the neural network are analyzed, including precision, accuracy, recall and the F1 score. All performance metrics consider the number of epochs for the training process, obtaining as a result a model that classifies between three groups: faces without face mask, faces wearing a face mask improperly and faces wearing a mask properly. with a great performance in all metrics; The results show values greater than 85% for precision, recall and F1 score, and accuracy values between 93% for 5 epochs and 95% for 25 epochs.

scholarly journals Identify Attractive and Unattractive Individuals Based on Geometric Features Using Neural Network

2021 ◽  
Vol 38 (4) ◽  
pp. 1007-1012
Author(s):  
Shakiba Ahmadimehr ◽  
Mohammad Karimi Moridani
Keyword(s):  
Neural Network ◽  
Facial Attractiveness ◽  
Lateral Position ◽  
Geometric Features ◽  
Principle Components Analysis ◽  
Facial Beauty ◽  
The Neural Network ◽  
The Face ◽  
Classification Evaluation ◽  
Facial Images

This paper aims to explore the essence of facial attractiveness from the viewpoint of geometric features toward the classification and identification of attractive and unattractive individuals. We present a simple but useful feature extraction for facial beauty classification. Evaluation of facial attractiveness was performed with different combinations of geometric facial features using the deep learning method. In this method, we focus on the geometry of a face and use actual faces for our analysis. The proposed method has been tested on, image database containing 60 images of men's faces (attractive or unattractive) ranging from 20-50 years old. The images are taken from both frontal and lateral position. In the next step, principle components analysis (PCA) was applied to feature a reduction of beauty, and finally, the neural network was used for judging whether the obtained analysis of various faces is attractive or not. The results show that one of the indexes in identifying facial attractiveness base of science, is the values of the geometric features in the face, changing facial parameters can change the face from unattractive to attractive and vice versa. The experimental results are based on 60 facial images, high accuracy of 88%, and Sensitivity of 92% is obtained for 2-level classification (attractive or not).

scholarly journals Crowd Detection by Video Processing

2020 ◽  
pp. 119-125
Author(s):  
S. Gokulraj ◽  
Soundarya. B
Keyword(s):  
Video Processing ◽  
Identification Problem ◽  
Target Face ◽  
Video Frame ◽  
Face Identification ◽  
Single Shot ◽  
Face Images ◽  
Video Frames ◽  
The Face ◽  
Bounding Boxes

The video may consist of multiple shots. The target in the probe video is only annotated once with a face bounding box in a frame. Most video face identification techniques assume that the video is of single shot, and thus the bounding boxes of the target face can be extracted by tracking a face across the video frames. Nevertheless, such automatic annotation is vulnerable to the drifting of the face tracker, and the face tracking algorithm is inadequate to associate the face images of the target across multiple shots. A target face association (TFA) technique retrieves a set of representative face images in a given video that are likely to have the same identity as the target face. These face images are then utilized to construct a robust face representation of the target face for searching the corresponding subject in the gallery. Since two faces that appear in the same video frame cannot belong to the same person, such cannot-link constraints are utilized for learning a target-specific linear classifier for establishing the intra/ inter-shot face association of the target. Experimental results on the newly released JANUS challenge set 3 (JANUS CS3) dataset show that TFA method generates robust representations from target-annotated videos and demonstrates good performance for the task of video-based face identification problem.

Numerical treatment of squeezing unsteady nanofluid flow using optimized stochastic algorithm

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ahcene Nouar ◽  
Amar Dib ◽  
Mohamed Kezzar ◽  
Mohamed R. Sari ◽  
Mohamed R. Eid
Keyword(s):  
Neural Network ◽  
Interior Point Method ◽  
Good Accuracy ◽  
Numerical Solutions ◽  
Stochastic Algorithm ◽  
Activation Functions ◽  
Nanofluid Flow ◽  
Discrete Form ◽  
The Neural Network ◽  
Field Velocity

Abstract In this paper, very efficient, intelligent techniques have been used to solve the fourth-order nonlinear ordinary differential equations arising from squeezing unsteady nanofluid flow. The activation functions used to develop the three models are log-sigmoid, radial basis, and tan-sigmoid. The neural network of each scheme is optimized with the interior point method (IPM) to find the weights of the networks. The confrontation of the obtained results with the numerical solutions shows good accuracy of the three schemes. The obtained solutions by utilizing the neural network technique of our variables field (velocity and temperature) are continuous contrary to the discrete form obtained by the numerical scheme.

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