scholarly journals Optimized Multi-Model Biometric Based Human Authentication using Deep Neural Network

2019 ◽  
Vol 8 (3S3) ◽  
pp. 280-290
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Histogram Equalization ◽  
Extraction Process ◽  
Input Image ◽  
Suggested Approach ◽  
Biometric Systems ◽  
Recognition Systems ◽  
The Right ◽  
Facial Identification

Biometrics provides greater security and usability than conventional personal authentication methods. Fingerprints, facial identification systems and voice recognition systems are the features that biometric systems can use. To improve biometric authentication, the proposed method considered that the input image is iris and fingerprint; at first, pre-processing is performed through histogram equalization for all image inputs to enhance the image quality. Then the extraction process of the feature will be performed. The suggested method uses modified Local Binary Pattern (MLBP), GLCM with orientation transformation, and DWT features next to the extracted features to be combined for feature extraction. Then the optimum function is found with the Rider Optimization Algorithm (ROA) for all MLBP, GLCM and DWT. Eventually, the approach suggested is accepted. Deep Neural Network (DNN) performs the proposed authentication process. A DNN is a multilayered artificial neural network between the layers of input and output. The DNN finds the right mathematical manipulation to turn the input into the output, whether it is an acknowledged image or not. Suggested process quality is measured in terms of reliability recognition. In the MATLAB platform, the suggested approach is implemented.

Comparison of Input Image Dimensions for Ship Detection from KOMPSAT-5 SAR Image Using Deep Neural Network

2020 ◽  
Vol 102 (sp1) ◽  
Author(s):  
Wook Park ◽  
Won-Kyung Baek ◽  
Joong-Sun Won ◽  
Hyung-Sup Jung
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Input Image ◽  
Sar Image ◽  
Ship Detection

Acoustic Scenery Recognition Using CWT and Deep Neural Network

2021 ◽  
Author(s):  
Francisco Mondragon ◽  
Jonathan Jimenez ◽  
Mariko Nakano ◽  
Toru Nakashika ◽  
Hector Perez-Meana
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Color Image ◽  
Classification Performance ◽  
Science And Engineering ◽  
Time Frequency ◽  
Frequency Representation ◽  
Public Data ◽  
Recognition Systems ◽  
Color Map

The development of acoustic scenes recognition systems has been a topic of extensive research due to its applications in several fields of science and engineering. This paper proposes an environmental system in which firstly a time-frequency representation is obtained using the Continuous Wavelet Transform (CWT). The time frequency representation is then represented as a color image using the Viridis color map, which is then inserted into a Deep Neural Network (DNN) to carry out the classification task. Evaluation results using several public data bases show that proposed scheme provides a classification performance better than the performance provided by other previously proposed schemes.

scholarly journals A Multi-Column Deep Framework for Recognizing Artistic Media

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1277
Author(s):  
Yang ◽  
Min
Keyword(s):  
Neural Network ◽  
Computer Graphics ◽  
Deep Neural Network ◽  
Input Image ◽  
Local Scale ◽  
Gram Matrix ◽  
Final Decision ◽  
Texture Information ◽  
Artistic Medium ◽  
Image Datasets

We present a multi-column structured framework for recognizing artistic media from artwork images. We design the column of our framework using a deep neural network. Our key idea is to recognize the distinctive stroke texture of an artistic medium, which plays a key role in distinguishing artistic media. Since stroke texture is in a local scale, the whole image is not proper for recognizing the texture. Therefore, we devise two ideas for our framework: Sampling patches from an input image and employing a Gram matrix to extract the texture. The patches sampled from an input artwork image are processed in the columns of our framework to make local decisions on the patch, and the local decisions from the patches are merged to make a final decision for the input artwork image. Furthermore, we employ a Gram matrix, which is known to effectively capture texture information, to improve the accuracy of recognition. Our framework is trained and tested using two real artwork image datasets: WikiSet of traditional artwork images and YMSet of contemporary artwork images. Finally, we build SynthSet, which is a collection of synthesized artwork images from many computer graphics literature, and propose a guideline for evaluating the synthesized artwork images.

scholarly journals A novel deep neural network for hidden target detection in images

2021 ◽  
Author(s):  
Rabeb Hendaoui ◽  
◽  
Vasif Nabiyev ◽  
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Deep Neural Network ◽  
Target Identification ◽  
Input Image ◽  
Second Phase ◽  
Natural Environments ◽  
Significant Similarity ◽  
Target Segmentation ◽  
Two Phases

The significant similarity between the hidden target and the background makes it difficult to find camouflaged people, such as warriors in warfare, or even camouflaged objects in natural environments. Hence, it is hard to ascertain these concealed targets. To address this issue, a novel deep neural network is proposed in this paper that produces an estimated mask within the hidden target for an input image. Our approach consists of two phases: hidden target segmentation and hidden target identification. For the first phase, we propose the Multilevel Attention Network (MA-Net), which generates the camouflaged target mask based on a Multi-Attention Module (MAM) that helps distinguish the hidden people from the background. Later on, the concealed target will be highlighted in the second phase. Experimental results on the camouflaged people dataset demonstrate that our proposed method can achieve state-of-the-art performance for hidden target detection.

scholarly journals Simultaneous Monocular Visual Odometry and Depth Reconstruction with Scale Recovery

2020 ◽  
Author(s):  
Guoliang Liu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Visual Odometry ◽  
Input Image ◽  
View Synthesis ◽  
Depth Image ◽  
The Core ◽  
Depth Prediction ◽  
Camera Pose ◽  
Depth Reconstruction

In this paper, we propose a deep neural networkthat can estimate camera poses and reconstruct thefull resolution depths of the environment simultaneously usingonly monocular consecutive images. In contrast to traditionalmonocular visual odometry methods, which cannot estimatescaled depths, we here demonstrate the recovery of the scaleinformation using a sparse depth image as a supervision signalin the training step. In addition, based on the scaled depth,the relative poses between consecutive images can be estimatedusing the proposed deep neural network. Another novelty liesin the deployment of view synthesis, which can synthesize anew image of the scene from a different view (camera pose)given an input image. The view synthesis is the core techniqueused for constructing a loss function for the proposed neuralnetwork, which requires the knowledge of the predicted depthsand relative poses, such that the proposed method couples thevisual odometry and depth prediction together. In this way,both the estimated poses and the predicted depths from theneural network are scaled using the sparse depth image as thesupervision signal during training. The experimental results onthe KITTI dataset show competitive performance of our methodto handle challenging environments.<br>

Speaker and Speech Recognition using Deep Neural Network

2018 ◽  
Vol 6 (8) ◽  
pp. 118 ◽  
Author(s):  
Gurpreet Kaur ◽  
Mohit Srivastava ◽  
Amod Kumar
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Extraction ◽  
Speech Recognition ◽  
Deep Neural Network ◽  
Rate Sensitivity ◽  
Extraction Process ◽  
Recall Rate ◽  
Feature Extraction Method ◽  
Work Model

In command and control applications, feature extraction process is very important for good accuracy and less learning time. In order to deal with these metrics, we have proposed an automated combined speaker and speech recognition technique. In this paper five isolated words are recorded with four speakers, two males and two females. We have used the Mel Frequency Cepstral Coefficient (MFCC)  feature extraction method with Genetic Algorithm to optimize the extracted features and generate an appropriate feature set. In first phase, feature extraction using MFCC is executed following the feature optimization using Genetic Algorithm and in last & third phase, training is conducted using the Deep Neural Network. In the end, evaluation and validation of the proposed work model is done by setting real environment. To check the efficiency of the proposed work, we have calculated the parameters like accuracy, precision rate, recall rate, sensitivity and specificity..

scholarly journals Application of Deep Learning Approaches for Enhancing Mastcam Images

2020 ◽  
Author(s):  
Ying Qu ◽  
Hairong Qi ◽  
Chiman Kwan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Network Models ◽  
Learning Approaches ◽  
Neural Network Models ◽  
Mars Rover ◽  
The Right

There are two mast cameras (Mastcam) onboard the Mars rover Curiosity. Both Mastcams are multispectral imagers with nine bands in each. The right Mastcam has three times higher resolution than the left. In this chapter, we apply some recently developed deep neural network models to enhance the left Mastcam images with help from the right Mastcam images. Actual Mastcam images were used to demonstrate the performance of the proposed algorithms.

USAGE OF ADVERSARIAL EXAMPLES TO PROTECT A HUMAN IMAGE FROM BEING DETECTED BY RECOGNITION SYSTEMS BASED ON DEEP NEURAL NETWORKS

2020 ◽  
pp. 32-38
Author(s):  
S. A. Sakulin ◽  
A. N. Alfimtsev ◽  
D. A. Loktev ◽  
A. O. Kovalenko ◽  
V. V. Devyatkov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
High Efficiency ◽  
Recognition System ◽  
Human Observer ◽  
Adversarial Examples ◽  
Recognition Systems ◽  
Human Image

Recently, human recognition systems based on deep machine learning, in particular, on the basis of deep neural networks, have become widespread. In this regard, research has become relevant in the field of protection against recognition by such systems. In this article a method of designing a specially selected type of camouflage applied to clothing, which will protect a person both from recognition by a human observer and from a deep neural network recognition system is proposed. This type of camouflage is constructed on the basis of competitive examples that are generated by a deep neural network. The article describes experiments on human protection from recognition by Faster-RCNN (Regional Convolution Neural Networks) Inception V2 and Faster-RCNN ResNet101 systems. However, the implementation of camouflage is considered on a macro level, which assesses the combination of the camouflage and background, and the micro level which analyzes the relationship between the properties of individual regions of the camouflage properties of the adjacent regions, with constraints on their continuity, smoothness, closure, asymmetry. The dependence of camouflage characteristics on the conditions of observation of the object and the environment is also considered: the transparency of the atmosphere, the intensity of pixels of the sky horizon and the background, the level of contrast of the background and the camouflaged object, the distance to the object. As an example of a possible attack, a “black box” attack, which involves preliminary testing of generated adversarial examples on a target recognition system without knowledge of the internal structure of this system, is considered. Results of these experiments showed the high efficiency of the proposed method in the virtual world, when there is access to each pixel of the image supplied to the input systems. In the real world, results are less impressive, which can be explained by the distortion of colors when printing on the fabric, as well as the lack of spatial resolution of this print.

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