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Access Control System Based on Face recognition 7

Access Control System Based on Face Recognition

引用

7th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Shavetov, Sergei Sivtsov, Vladimir ITMO Univ Fac Control Syst & Robot Kronverkskiy Prospekt 49 St Petersburg 197101 Russia

ISBN: (纸本)9781728159539

Face recognition is a technology that uses face image of someone to verify his identity by finding this person in a given photos database. It becomes very practical in access control systems as it does not require any physical interaction for gaining access as traditional ways with keys. Moreover, these systems only require a camera for recognition and are easy to install and use. This is why they are already in use by companies as access control to their offices, in home automation systems, etc. In this paper, different approaches to face recognition are studied. The first step of any face recognition system is face detection and cropping so we analyzed classical Viola-Jones face detection and MultiTask Convolutional Neural Networks (MTCNN) in terms of detection quality and processing time. The final classifier obtained is capable of matching face from the online camera to image from a given database. We also considered decreasing the vulnerability of standalone face recognition by adding a spoof detection method so that the system does not react to every approach to bypass the system like showing a photo of an allowed person shown on a phone's screen.

关键词： face recognition face detection machine learning access control computer vision

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Sub-scene Target Detection and recognition Using Deep learning Convolution Neural Networks 1st

Sub-scene Target Detection and Recognition Using Deep Learni...

引用

1st international conference on Data Science, machine learning and Applications, 2019

作者： Merugu, Suresh Jain, Kamal Mittal, Anshul Raman, Balasubramanian R&D Centre CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9789811514197

Sub-scene recognition algorithm based on super resolution along with scene dependent neural network model and sub-scene dependent target detection for automating object information extraction is proposed. This work deals with large number of challenges possessed by classification problems. Some of the challenges in this problem are the low resolution satellite images, diverse pattern of each sub-scene causing the low level learning for classification and plethora of distinct object classes present in each sub-scene causes low accuracy of object detection. Objective of this paper presents an image super resolution technique for rectifying problems posed by low resolution images with color density variations of chromaticity coordinates. To eliminate the problem of diverse patterns, have divided various land cover types into separate groups based on maximum mixed fraction among these groups and corresponding sub-scene recognition disparate model parameters are used to recognize various scenes. To increase the accuracy for object detection has developed a sub-scene dependent Neural Network model for extracting the target/anomaly of object information. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Data Augmentation for a Deep learning Framework for Ventricular Septal Defect Ultrasound image Classification 25th

Data Augmentation for a Deep Learning Framework for Ventricu...

引用

25th international conference on pattern recognition Workshops, ICPR 2020

作者： Chen, Shih-Hsin Tai, I-Hsin Chen, Yi-Hui Weng, Ken-Pen Hsieh, Kai-Sheng Department of Information Management Cheng Shiu University Kaohsiung City83347 Taiwan Department of Pediatric Cardiology China Medical University Chidren’s Hospital Taichung City40447 Taiwan Department of Information Management Chang Gung University Taoyuan33302 Taiwan Kawasaki Disease Center Kaohsiung Chang Gung Memorial Hospital Kaohsiung83301 Taiwan Congenital Structural Heart Disease Center Department of Pediatrics Kaohsiung Veterans General Hospital No. 386 Dazhong 1st Road Zuoying District Kaohsiung City Taiwan Department of Pediatrics Shuang-Ho Hospital-Taipei Medical University New Taipei City23561 Taiwan

ISBN: (纸本)9783030687984

Congenital heart diseases (CHD) can be detected through ultrasound imaging. Although ultrasound can be used for immediate diagnosis, doctors require considerable time to read dynamic clips;typically, physicians must continuously examine disease data from beating heart images. Most importantly, this type of diagnosis relies heavily on the expertise and experience of the diagnosing physician. This study established an ultrasound image classification with deep learning algorithms to overcome the challenges involved in CHD diagnosis. We detected the most common CHD, namely the first, second, and fourth types of ventricular septal defect (VSD). We improved the performance levels of well-known deep learning algorithms (InceptionV3, ResNet, and DenseNet). Because algorithm optimization and overfitting problems can influence the performance of deep learning algorithms, we studied some optimizer algorithms and early-stopping strategies. To enhance the solution quality, we used data augmentation methods for solving this classification problem. The selected approach was further compared with Google AutoML, which applies structure search for quality prediction. Our results revealed that the proposed deep learning algorithm was able to recognize most types of VSD. However, one type of VSD remains unconquered and warrants more advanced techniques. © 2021, Springer Nature Switzerland AG.

关键词： image classification

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Surface flaw classification based on dual cross pattern 1

Surface flaw classification based on dual cross pattern

引用

1st international conference on Communications, Control Systems and Signal processing, CCSSP 2020

作者： Mentouri, Zoheir Doghmane, Hakim Moussaoui, Abdelkrim Boudjehem, Djalil Unit. Research Center in Industrial Technologies-CRTI Iron and Steel Applied Research P.O Box 64 Algiers Algeria Lab. of Inverse Problems Université 8 Mai 1945 BP 401 Guelma Algeria Lab. of Electrical Engineering Université 8 Mai 1945 BP 401 Guelma Algeria Lab. of Advanced Control Université 8 Mai 1945 BP 401 Guelma Algeria

ISBN: (数字)9781728158358

ISBN: (纸本)9781728158358

The evaluation of flat steel surface quality is mainly concerned with detecting and identifying product surface defects. Although the variety of the implemented techniques, this type of control still presents a challenge. In this paper, we assess the Dual Cross pattern technique, as a feature descriptor, that should be quite discriminative, to ease the steel surface defect classification. The histograms extracted from the captured DCP features are concatenated to represent the global image feature vector. The procedure parameters, as the DCP circle radius, the number of the training images and their choice, are considered to show their impact on the results. The experiment conducted on the NEU published defect database shows that, compared to the other used techniques, the proposed approach reveals not only interesting recognition rates but presents advantages in time coast too. © 2020 IEEE.

关键词： Surface defects

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Development of simulator for efficient aquaculture of Sillago japonica using reinforcement learning 1

Development of simulator for efficient aquaculture of Sillag...

引用

1st international conference on image processing and Robotics (ICIP)

作者： Kuroki, Haruki Ikeoka, Hiroshi Isawa, Koichi Fukuyama Univ Dept Comp Sci Fac Engn Fukuyama Hiroshima Japan Fukuyama Univ Dept Architecture Fac Engn Fukuyama Hiroshima Japan

ISBN: (纸本)9781728165417

Recently, the situation in the Japanese fishing industry has become critical, resulting in one of the most significant food issues in Japan. Aquaculture technology is expected to be a solution to this problem. Sillago japonica is a fish that inhabits shallow waters in parts of Asia, and large Sillago japonica is very expensive. Therefore, we believe that aquaculture of this fish would help revitalize the fishing industry in Japan. However, aquaculture requires considerable manual labor. Hence, we need to introduce new technologies for the aquaculture of Sillago japonica. Specifically, we have been developing two systems to improve efficiency and reduce costs of this aquaculture: an environment control system and an automatic feeding system. The former is to maintain favorable environment conditions for the fish in the aquaculture tank. The latter is for optimal feeding of the fish. In this paper, we describe the development of an automatic feeding system using artificial intelligence (AI). This system includes four processes: image input, image recognition, feeder control, and feeding action. We have adopted AI technologies to assist in the second and third processes. Although these two processes can be implemented together, it is easier for the AI to learn them as two separate processes. In particular, the second (image recognition) process uses supervised learning, and the third (feeder control) process uses reinforcement learning. However, it is impractical to train the AI in the third process in a real-world aquaculture environment that sustains many failures. Therefore, we have developed an aquaculture simulator to facilitate AI learning of the feeder control process. Additionally, we performed an experiment to validate our simulator using the number of feeders and the number of fish as a parameter.

关键词： artificial intelligence simulation reinforcement learning aquaculture

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Melanoma Skin Lesions Classification using Deep Convolutional Neural Network with Transfer learning

Melanoma Skin Lesions Classification using Deep Convolutiona...

引用

Artificial Intelligence and Data Analytics (CAIDA), international conference on

作者： Md. Khairul Islam Md Shahin Ali Md Mosahak Ali Mst. Farija Haque Abhilash Arjan Das Md. Maruf Hossain D S Duranta Md Afifur Rahman Islamic University Kushtia Bangladesh

ISBN: (纸本)9781665430418

Skin cancer is basically the unnatural growth of skin tissues and it can be fatal. Lately, it has evolved into one of the most perilous types of other cancers in the human body. Premature detection can help to endure the patient. Detection of skin cancer is quite difficult. At present in medical image diagnosis, the performance of computer vision is quite conducive. Together with the progress in technology and impetuous increment in computer provision, different types of machine learning techniques and deep learning models have arisen for the analysis of medical images particularly skin lesion images. In this study, we propose a deep learning model with some image pre-processing steps that help to categorize skin lesions with a better classification rate than other existing models. Normalization, data reduction, and data augmentation are used in pre-processing steps to classify benign and malignant cancer lesions from the HAM10000 dataset. From the experimental result, the proposed model gained an accuracy of 96.10% in training and 90.93% during testing. This model reduces the execution time and performs well-handled.

关键词： Deep learning Training Computational modeling Transfer learning Melanoma Skin Lesions

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image Colorization Using Generative Adversarial Networks and Transfer learning 1

Image Colorization Using Generative Adversarial Networks and...

引用

1st international conference on machine Vision and image processing, MVIP 2020

作者： Kiani, Leila Saeed, Masoud Nezamabadi-Pour, Hossein Shahid Bahonar University of Kerman Computer Engineering Department Kerman Iran Kerman Iran

ISBN: (纸本)9781728168326

Automatic colorizing is one of the most interesting problems in computer graphics. During the colorization process, the gray one-dimensional images are converted to three-dimensional images with colored components. As a typical technique, Convolutional neural networks (CNNs) have been well studied and used for automatic coloring. In these networks, the information that is generalized over in the top layers is available in intermediate layers. Although the output of the last layer of CNNs is usually used in many applications, in this paper, we use a concept called Hypercolumn derived from neuroscience to exploit information at all levels to develop a fully automated image colorization system. There are not always millions of data available in the real world to train complex deep learning models. Therefore, the VGG19 model trained with the big data set of imageNet is used as a pre-trained model in the generator network and the hypercolumn idea is implemented in it with DIV2K datasets. We train our model to predict each pixel's color texture. The results obtained indicate that the proposed method is superior to competing models. © 2020 IEEE.

关键词： Convolutional neural networks

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Efficient eye diagram analyzer for optical modulation format recognition using deep learning technique 1st

Efficient eye diagram analyzer for optical modulation format...

引用

1st international conference on Advances in Electrical and Computer Technologies, ICAECT 2019

作者： Ghayal, Viay Sudhakar Jeyachitra, R.K. Department of Electronics and Communication Engineering National Institute of Technology TiruchirappalliTamil Nadu620015 India

ISBN: (纸本)9789811555572

A convolutional neural network (CNN)-based deep learning technique is proposed to implement recognition of optical modulation formats. CNN is used to implement an intelligent eye diagram analyzer using which modulation format recognition (MFR) is achieved. CNN can extract and self-learn the features of an image in its raw format. With this ability, CNN is used to process eye diagram images from the perspective of image processing. Signals of six different widely used modulation formats are passed through the noisy channel. At receiver, eye diagram images of signals whose optical signal-to-noise rate (OSNR) ranges from 10 to 25 dB are simulated. The accuracies of MFR achieve 100%. The proposed technique has the potential to be used at receiver side for intelligent signal analysis and optical performance monitoring. © Springer Nature Singapore Pte Ltd 2020.

关键词： Deep learning

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Source Camera Identification Using WLBP Descriptor 1

Source Camera Identification Using WLBP Descriptor

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1st international conference on machine Vision and image processing, MVIP 2020

作者： Zandi, Nasme Razzazi, Farbod Department of Electrical and Computer Engineering Islamic Azad University Science and Research Branch Tehran Iran

In this paper we introduce a camera identification method using WLBP texture descriptor. This descriptor has previously been used for texture and face classifiers. In the proposed method, we proposed to use WLBP operator in camera classification application to identify the imaging camera. In our method, the two-dimensional histogram of Weber's features and LBP for camera identification are investigated. For this purpose, experiments were conducted on Dresden database. The proposed method has reached the accuracy of 99.52% on nine digital cameras of different models. In compressed JPEG images with the compression quality factor of 70% the method reached the accuracy of 89.04%. The results indicate that the proposed method has a high degree of accuracy in comparison to other proposed method and exhibits relatively good robustness to compression. © 2020 IEEE.

关键词： Local binary pattern

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Deep learning for Agricultural Plant Disease Detection 1st

Deep Learning for Agricultural Plant Disease Detection

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1st international conference on Data Science, machine learning and Applications, 2019

作者： Giraddi, Shantala Desai, Shrinivas Deshpande, Anupama KLE Technological University Hubli India

ISBN: (纸本)9789811514197

Maize is the third significant crop in India. A number of fungal deceases are affecting Maize. The deceases Northern Blight, Southern Blight and Rust are causing reduction in the yield to an extent of 28–91%. The symptoms of these diseases appear on the leaves usually at the flowering stage. In this paper, study on automated detection of fungal disease in maize leaves is carried out. The proposed system uses image processing techniques using deep learning. The results obtained are encouraging with an accuracy of 98% for three class problems and accuracy of 70.5% for four class problems. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image processing

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