scholarly journals Hand Recognition Using Thermal Image and Extension Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Meng-Hui Wang
Keyword(s):  
Neural Network ◽  
Infrared Camera ◽  
Recognition System ◽  
Thermal Image ◽  
Recognition Method ◽  
Good Tolerance ◽  
Personal Recognition ◽  
Recognition Systems ◽  
Hand Recognition ◽  
High Degree

Hand recognition is one of the popular biometry methods for access control systems. In this paper, a new scheme for personal recognition using thermal images of the hand and an extension neural network (ENN) is presented. The features of the recognition system are extracted from gray level hand images, which are taken by an infrared camera. The main advantage of the thermal image is that it can reduce errors and noise in the features extracted stage, which is most important to increase the accuracy of recognition systems. Moreover, a new recognition method based on the ENN is proposed to perform the core functions of the hand recognition system. The proposed ENN-based recognition method also permits rapid adaptive processing for a new pattern, as it only tunes the boundaries of classified features or adds a new neural node. It is feasible to implement the proposed method on a Microcomputer for a portable personal recognition device. From the tested examples, the proposed method has a significantly high degree of recognition accuracy and shows good tolerance to errors added.

scholarly journals Individual identification of Japanese macaques (Macaca fuscata) using a face recognition system and a limited number of learning images

2020 ◽  
Author(s):  
Yosuke Otani ◽  
Hitoshi Ogawa
Keyword(s):  
Face Recognition ◽  
Macaca Fuscata ◽  
Field Research ◽  
Recognition System ◽  
Japanese Macaques ◽  
Individual Identification ◽  
System Construction ◽  
Face Recognition System ◽  
Recognition Systems ◽  
High Degree

AbstractIndividual identification is an important technique in animal research that requires researcher training and specialized skillsets. Face recognition systems using artificial intelligence (AI) deep learning have been put into practical use to identify in humans and animals, but a large number of annotated learning images are required for system construction. In wildlife research cases, it is difficult to prepare a large amount of learning images, which may be why systems using AI have not been widely used in field research. To investigate the development of a system that identifies individuals using a small number of learning images, we constructed a system to identify individual Japanese macaques (Macaca fuscata yakui) with a low error rate from an average of 20 images per individual. The characteristics of this system were augmentation of data, simultaneous determination by four individual identification models and identification from a majority of five frames to ensure reliability. This technology has a high degree of utility for various stakeholders and it is expected that it will advance the development of individual identification systems by AI that can be widely used in field research.

scholarly journals A Genetic-LVQ neural networks approach for handwritten Arabic character recognition

2018 ◽  
Vol 7 (2) ◽  
pp. 43
Author(s):  
Abir Alharbi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Character Recognition ◽  
Recognition System ◽  
Smart Devices ◽  
Binary Matrix ◽  
Recognition Systems ◽  
Handwritten Recognition ◽  
Handwritten Arabic

Handwritten recognition systems are a dynamic field of research in areas of artificial intelligence. Many smart devices available in the market such as pen-based computers, tablets, mobiles with handwritten recognition technology need to rely on efficient handwritten recognition systems. In this paper we present a novel Arabic character handwritten recognition system based on a hybrid method consisting of a genetic algorithm and a Learning vector quantization (LVQ) neural network. Sixty different handwritten Arabic character datasets are used for training the neural network. Each character dataset contains 28 letters written twice with 15 distinct shaped alphabets, and each handwritten Arabic letter is represented by a binary matrix that is used as an input to a genetic algorithm for feature selection and dimension reduction to include only the most effective features to be fed to the LVQ classifier. The recognition process in the system involves several essential steps such as: handwritten letter acquisition, dataset preparation, feature selection, training, and recognition. Comparing our results to those acquired by the whole feature dataset without selection, and to the results using other classification algorithms confirms the effectiveness of our proposed handwritten recognition system with an accuracy of 95.4%, hence, showing a promising potential for improving future handwritten Arabic recognition devices in the market.

A Hybrid Approach For Automatic Licence Plate Recognition System

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Sharma ◽  
Pawan Kumar Dahiya ◽  
Baldev Raj Marwah
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Recognition System ◽  
Traffic Monitoring ◽  
Support Vector ◽  
Network Support ◽  
Recognition Systems ◽  
Licence Plate Recognition

: Automatic licence plate recognition systems are used for various applications such as traffic monitoring, toll collection, car parking, law enforcement. In this paper, a convolutional neural network and support vector machine based automatic licence plate recognition system is proposed. Firstly, The characters extracts from the input image of vehicle. Then characters are segment and their features are extracts. The extracted features are classified using convolutional neural network and support vector machine for the final recognition of the licence plate. The obtained recognition rate by the hybridization of the convolutional neural network and the support vector machine is 96.5%. The recognition rate obtained for the proposed hybrid automatic licence plate system are compared with three other automatic licence plate systems based on neural network, support vector machine, and convolutional neural network. The proposed automatic licence plate recognition system perform better than the neural network, support vector machine, and convolutional nerural network based automatic licence plate recognition systems.

scholarly journals An effective face recognition method using guided image filter and convolutional neural network

2021 ◽  
Vol 23 (3) ◽  
pp. 1699
Author(s):  
Yallamandaiah S. ◽  
Purnachand N.
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Recognition Method ◽  
Image Filter ◽  
Guided Image Filter ◽  
Face Region ◽  
The Face ◽  
Recognition Systems

<p>In the area of computer vision, face recognition is a challenging task because of the pose, facial expression, and illumination variations. The performance of face recognition systems reduces in an unconstrained environment. In this work, a new face recognition approach is proposed using a guided image filter, and a convolutional neural network (CNN). The guided image filter is a smoothing operator and performs well near the edges. Initially, the ViolaJones algorithm is used to detect the face region and then smoothened by a guided image filter. Later the proposed CNN is used to extract the features and recognize the faces. The experiments were performed on face databases like ORL, JAFFE, and YALE and attained a recognition rate of 98.33%, 99.53%, and 98.65% respectively. The experimental results show that the suggested face recognition method attains good results than some of the state-of-the-art techniques.</p>

scholarly journals Speech Recognition of Isolated Words using a New Speech Database in Sylheti

2019 ◽  
Vol 8 (3) ◽  
pp. 6259-6268
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Natural Languages ◽  
Speech Database ◽  
Recognition Systems ◽  
Neural Network Classifiers ◽  
Engineering Psychology

With the advancements in the field of artificial intelligence, speech recognition based applications are becoming more and more popular in the recent years. Researchers working in many areas including linguistics, engineering, psychology, etc. have been trying to address various aspects relating to speech recognition in different natural languages around the globe. Although many interactive speech applications in "well-resourced" major languages are being developed, uses of these applications are still limited due to language barrier. Hence, researchers have also been concentrating to design speech recognition system in various under-resourced languages. Sylheti is one of such under-resourced languages primarily spoken in the Sylhet division of Bangladesh and also spoken in the southern part of Assam, India. This paper has two contributions: i) it presents a new speech database of isolated words for the Sylheti language, and ii) it presents speech recognition systems for the Sylheti language to recognize isolated Sylheti words by applying two variants of neural network classifiers. The performances of these recognition systems are evaluated with the proposed database and the observations are presented.

scholarly journals RECOGNITION METHOD OF ELLIPTIC FORMS OBJECTS ON THE IMAGES

2019 ◽  
Vol 2019 (3) ◽  
pp. 4-8
Author(s):  
Евгений Леонов ◽  
Evgeny Leonov ◽  
Юрий Леонов ◽  
Yuriy Leonov ◽  
Андрей Аверченков ◽  
...  
Keyword(s):  
Mobile Devices ◽  
High Speed ◽  
Video Camera ◽  
Recognition System ◽  
Recognition Method ◽  
Computing Power ◽  
The Road ◽  
Road Signs ◽  
Recognition Systems ◽  
Intelligent Recognition

The article briefly describes the methodology and suggests the method for recognizing any elliptic forms objects on the images. This method is universal and can be applied in any intelligent recognition systems, for example, recognition system of the road signs from video camera images. The proposed method has proven itself in solving various practical problems, such as searching for signs in photographs, detecting circles on charts and diagrams, searching for the boundaries of ovals of faces, etc. The main advantage of the method is its extreme ease of implementation and high speed, which makes it possible to use not only on modern stationary computers, but also on mobile devices with low computing power.

Speaker Recognition System based on Age-related Features using Convolutional and Deep Neural Networks

2020 ◽  
Author(s):  
Karthika Kuppusamy ◽  
Chandra Eswaran
Keyword(s):  
Neural Network ◽  
Speaker Recognition ◽  
Speech Rate ◽  
Voice Recognition ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Support Vector ◽  
Age Related ◽  
Novel Approach ◽  
Recognition Systems

Abstract With the advent of conversational voice recognition systems growing such as Alexa, SIRI, OK Google, etc., natural language conversational systems including Chatbot and voice recognition systems are in new high and determining the age of a speaker is critical for setting the pertinent context. Age can be inferred from the speech signal by inferring various factors such as physical attributes of voice, linguistic attributes, frequency, speech rate,etc., The proposed research article discusses about extracting the spectral features of speech such as Cepstral Coefficients, Spectral Decrease, Centroid, Flatness, Spectral Entropy, F0DIFF, Jitter and Shimmer as inputs. This would help in classifying speaker age through deep learning techniques. A novel approach is addressed along with the model for implementation using Deep Neural Network and Convolutional Neural Network for classifying the features using three different classifiers which are Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and GMM-SVM. The results obtained from the proposed system would outline the performance in speaker age recognition.

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.

scholarly journals Face Recognition System Against Adversarial Attack Using Convolutional Neural Network

2021 ◽  
Vol 18 (1) ◽  
pp. 1-8
Author(s):  
Ansam Kadhim ◽  
Salah Al-Darraji
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition System ◽  
Face Recognition System ◽  
The Face ◽  
Recognition Systems ◽  
Projected Gradient Descent ◽  
Employee Attendance ◽  
Sign Method

Face recognition is the technology that verifies or recognizes faces from images, videos, or real-time streams. It can be used in security or employee attendance systems. Face recognition systems may encounter some attacks that reduce their ability to recognize faces properly. So, many noisy images mixed with original ones lead to confusion in the results. Various attacks that exploit this weakness affect the face recognition systems such as Fast Gradient Sign Method (FGSM), Deep Fool, and Projected Gradient Descent (PGD). This paper proposes a method to protect the face recognition system against these attacks by distorting images through different attacks, then training the recognition deep network model, specifically Convolutional Neural Network (CNN), using the original and distorted images. Diverse experiments have been conducted using combinations of original and distorted images to test the effectiveness of the system. The system showed an accuracy of 93% using FGSM attack, 97% using deep fool, and 95% using PGD.

Comparative Analysis of Various Soft Computing Technique-Based Automatic Licence Plate Recognition Systems

2021 ◽  
pp. 18-37
Author(s):  
Nitin Sharma ◽  
Pawan Kumar Dahiya ◽  
B. R. Marwah
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
Support Vector ◽  
Intelligent Transport System ◽  
Hybrid Technique ◽  
Network Support ◽  
Recognition Systems ◽  
Licence Plate Recognition

Traffic on Indian roads is growing day by day leading to accidents. The intelligent transport system is the solution to resolve the traffic problem on roads. One of the components of the intelligent transportation system is the monitoring of traffic by the automatic licence plate recognition system. In this chapter, a automatic licence plate recognition systems based on soft computing techniques is presented. Images of Indian vehicle licence plates are used as the dataset. Firstly, the licence plate region is extracted from the captured image, and thereafter, the characters are segmented. Then features are extracted from the segmented characters which are used for the recognition purpose. Furthermore, artificial neural network, support vector machine, and convolutional neural network are used and compared for the automatic licence plate recognition. The future scope is the hybrid technique solution to the problem.

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