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Journal articles on the topic 'Deep learning,face detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Cárdenas, Rolando J., Cesar A. Beltrán, and Juan C. Gutiérrez. "Small Face Detection Using Deep Learning on Surveillance Videos." International Journal of Machine Learning and Computing 9, no. 2 (2019): 189–94. http://dx.doi.org/10.18178/ijmlc.2019.9.2.785.

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Prabu, Janani, Sai Saranesh, and Dr S. Ajitha. "Face Counting and Mask Detection using Deep Learning." International Journal of Scientific & Engineering Research 11, no. 12 (2020): 709–14. http://dx.doi.org/10.14299/ijser.2020.12.05.

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Face is one among the foremost important human's biometrics which is used frequently in every day human communication and due to some of its unique characteristics plays a major role in conveying identity and emotion. So far numerous methods have been proposed for face recognition, but it's still remained very challenging in real world applications and up to date; there is no technique which equals human ability to recognize faces despite many variations in appearance that the face can have in a scene and provides a strong solution to all situations.

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3

Liu, Weiwei. "Video Face Detection Based on Deep Learning." Wireless Personal Communications 102, no. 4 (2018): 2853–68. http://dx.doi.org/10.1007/s11277-018-5311-7.

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4

Pinki and Prof. Sachin Garg. "Face Mask Detection System using Deep Learning." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 161–64. http://dx.doi.org/10.46501/ijmtst061231.

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In the present scenario due to Covid-19, there is no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed

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Chandrasekaran, Anish. "Drowsy Face Detection using Deep Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2174–79. http://dx.doi.org/10.22214/ijraset.2021.35526.

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An important aspect of machine vision and image processing could be drowsiness detection system due to its high significance. In recent years there have been many research projects reported in the literature in this field.In this paper unlike the conventional drowsiness detection methods using machine learning we used deep learning techniques.Driver drowsiness results in many car crashes and fatalities worldwide.Whereas drowsiness in online attendees results in less attention span and decrease in the learning capabilities, such as meetings, lectures, webinars held. The advancement in computing

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Zhang, Huijun, Ling Feng, Ningyun Li, Zhanyu Jin, and Lei Cao. "Video-Based Stress Detection through Deep Learning." Sensors 20, no. 19 (2020): 5552. http://dx.doi.org/10.3390/s20195552.

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Stress has become an increasingly serious problem in the current society, threatening mankind’s well-beings. With the ubiquitous deployment of video cameras in surroundings, detecting stress based on the contact-free camera sensors becomes a cost-effective and mass-reaching way without interference of artificial traits and factors. In this study, we leverage users’ facial expressions and action motions in the video and present a two-leveled stress detection network (TSDNet). TSDNet firstly learns face- and action-level representations separately, and then fuses the results through a stream wei

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Li, Ming, Chengyang Yu, Fuzhong Nian, and Xiaoxu Li. "A Face Detection Algorithm Based on Deep Learning." International Journal of Hybrid Information Technology 8, no. 11 (2015): 285–96. http://dx.doi.org/10.14257/ijhit.2015.8.11.24.

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Ravidas, Shivkaran, and M. A. Ansari. "Deep learning for pose-invariant face detection in unconstrained environment." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 577. http://dx.doi.org/10.11591/ijece.v9i1.pp577-584.

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<span lang="EN-US">In the recent past, convolutional neural networks (CNNs) have seen resurgence and have performed extremely well on vision tasks. Visually the model resembles a series of layers each of which is processed by a function to form a next layer. It is argued that CNN first models the low level features such as edges and joints and then expresses higher level features as a composition of these low level features. The aim of this paper is to detect multi-view faces using deep convolutional neural network (DCNN). Implementation, detection and retrieval of faces will be obtained

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Sawat, Dattatray D., and Ravindra S. Hegadi. "Unconstrained face detection: a Deep learning and Machine learning combined approach." CSI Transactions on ICT 5, no. 2 (2016): 195–99. http://dx.doi.org/10.1007/s40012-016-0149-1.

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10

Wang, Pin, Peng Wang, and En Fan. "Violence detection and face recognition based on deep learning." Pattern Recognition Letters 142 (February 2021): 20–24. http://dx.doi.org/10.1016/j.patrec.2020.11.018.

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Abayomi-Alli, Olusola Oluwakemi, Robertas Damaševičius, Rytis Maskeliūnas, and Sanjay Misra. "Few-Shot Learning with a Novel Voronoi Tessellation-Based Image Augmentation Method for Facial Palsy Detection." Electronics 10, no. 8 (2021): 978. http://dx.doi.org/10.3390/electronics10080978.

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Face palsy has adverse effects on the appearance of a person and has negative social and functional consequences on the patient. Deep learning methods can improve face palsy detection rate, but their efficiency is limited by insufficient data, class imbalance, and high misclassification rate. To alleviate the lack of data and improve the performance of deep learning models for palsy face detection, data augmentation methods can be used. In this paper, we propose a novel Voronoi decomposition-based random region erasing (VDRRE) image augmentation method consisting of partitioning images into ra

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Virmani, D., P. Girdhar, P. Jain, and P. Bamdev. "FDREnet: Face Detection and Recognition Pipeline." Engineering, Technology & Applied Science Research 9, no. 2 (2019): 3933–38. http://dx.doi.org/10.48084/etasr.2492.

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Face detection and recognition are being studied extensively for their vast applications in security, biometrics, healthcare, and marketing. As a step towards presenting an almost accurate solution to the problem in hand, this paper proposes a face detection and face recognition pipeline - face detection and recognition embedNet (FDREnet). The proposed FDREnet involves face detection through histogram of oriented gradients and uses Siamese technique and contrastive loss to train a deep learning architecture (EmbedNet). The approach allows the EmbedNet to learn how to distinguish facial feature

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Satpute, Prachi. "Bare Face Person Recognition System using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 329–37. http://dx.doi.org/10.22214/ijraset.2021.34746.

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Nowadays, maintaining a good hygiene is very important to prevent many diseases like Corona Virus Disease (COVID-19). It has been rapidly affected our day-today life by disrupting the world trade and movements. The World Health Organization (WHO) recommend to the world that all people must wear a mask to prevent COVID-19. The use of masks is part of a comprehensive package of prevention and control measures that can limit the spread of certain respiratory viral diseases. Wearing a protective mask has become a new normal and beneficial for human being to avoid certain diseases. In the near futu

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Kowalski, Marcin, and Krzysztof Mierzejewski. "Detection of 3D face masks with thermal infrared imaging and deep learning techniques." Photonics Letters of Poland 13, no. 2 (2021): 22. http://dx.doi.org/10.4302/plp.v13i2.1091.

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Biometric systems are becoming more and more efficient due to increasing performance of algorithms. These systems are also vulnerable to various attacks. Presentation of falsified identity to a biometric sensor is one the most urgent challenges for the recent biometric recognition systems. Exploration of specific properties of thermal infrared seems to be a comprehensive solution for detecting face presentation attacks. This letter presents outcome of our study on detecting 3D face masks using thermal infrared imaging and deep learning techniques. We demonstrate results of a two-step neural ne

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Dhawale, Shweta Panjabrao. "Face with Mask Detection and Recognition for Smart Attendance System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1586–91. http://dx.doi.org/10.22214/ijraset.2021.36615.

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In this paper we will see the face mask detection and recognition for smart attendance system. In current pandemic situation our proposed system is very useful. We have used here face algorithm technique, python programming and to capture the images open cv is used., open cv2 now comes with a very new face recognizer class for the face recognition and popular computer vision liberaay started by intel in 1999. Open cv released under BSD licence that’s why used in the academic projects. We have used the concept of deep learning framework for the detection of faces. our aim is to present the stud

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Luo, Dazhi, Guihua Wen, Danyang Li, Yang Hu, and Eryang Huan. "Deep-learning-based face detection using iterative bounding-box regression." Multimedia Tools and Applications 77, no. 19 (2018): 24663–80. http://dx.doi.org/10.1007/s11042-018-5658-5.

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17

Sun, Xudong, Pengcheng Wu, and Steven C. H. Hoi. "Face detection using deep learning: An improved faster RCNN approach." Neurocomputing 299 (July 2018): 42–50. http://dx.doi.org/10.1016/j.neucom.2018.03.030.

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18

Mudassar Ilyas, Qazi, and Muneer Ahmad. "An Enhanced Deep Learning Model for Automatic Face Mask Detection." Intelligent Automation & Soft Computing 31, no. 1 (2022): 241–54. http://dx.doi.org/10.32604/iasc.2022.018042.

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19

Ismail, Aya, Marwa Elpeltagy, Mervat S. Zaki, and Kamal Eldahshan. "A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost." Sensors 21, no. 16 (2021): 5413. http://dx.doi.org/10.3390/s21165413.

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Currently, face-swapping deepfake techniques are widely spread, generating a significant number of highly realistic fake videos that threaten the privacy of people and countries. Due to their devastating impacts on the world, distinguishing between real and deepfake videos has become a fundamental issue. This paper presents a new deepfake detection method: you only look once–convolutional neural network–extreme gradient boosting (YOLO-CNN-XGBoost). The YOLO face detector is employed to extract the face area from video frames, while the InceptionResNetV2 CNN is utilized to extract features from

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Et al., Rohan Katari. "A Comparative Analysis of Variant Deep Learning Models for COVID-19 Protective Face Mask Detection." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 2841–48. http://dx.doi.org/10.17762/turcomat.v12i6.5791.

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The world is in the midst of a paramount pandemic owing to the rapid dissemination of coronavirus disease (COVID-19) brought about by the spread of the virus ‘SARS-CoV-2’. It is mainly transmitted among persons through airborne diffusion of droplets containing the virus produced by an infected person sneezing or coughing without covering their face. The World Health Organization (WHO) has issued numerous guidelines which state that the spread of this disease can be limited by people shielding their faces with protective face masks when in public or in crowded areas. As a precautionary measure,

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21

Dhaya, R. "Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach." March 2021 3, no. 2 (2021): 107–21. http://dx.doi.org/10.36548/jucct.2021.2.004.

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The World Health Organization (WHO) considers the COVID-19 Coronavirus to be a global pandemic. The most effective form of protection is to wear a face mask in public places. Moreover, the COVID-19 pandemic prompted all the countries to set up a lockdown to prevent viral transmission. According to a survey study, the use of facemasks at work decreases the chances of fast transmission. If the facemasks are not used or are worn incorrectly, it contributes to the third and fourth waves of the corona virus spreading throughout the world. This motivates us to conduct an efficient investigation of t

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22

M. Almufti, Saman, Ridwan B. Marqas, Zakiya A. Nayef, and Tamara S. Mohamed. "Real Time Face-mask Detection with Arduino to Prevent COVID-19 Spreading." Qubahan Academic Journal 1, no. 2 (2021): 39–46. http://dx.doi.org/10.48161/qaj.v1n2a47.

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The rise of COVID-19 pandemic has had a lasting impact in many countries worldwide since 2019. Face-mask detection had been significant progress in the Image processing and deep learning fields studies. Many face detection models have been designed using different algorithms and techniques. The proposed approach in this paper developed to avoid mask-less people from entering to a desired places (i.e. Mall, University, Office, …etc.) by detecting face mask using deep learning, TensorFlow, Keras, and OpenCV and sending a signal to Arduino device that connected to the gate to be open. it detect a

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23

Chaudhari, V. J. "Face Recognition and Emotion Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4775–77. http://dx.doi.org/10.22214/ijraset.2021.35698.

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This Face recognition and facial emotion detection is new era of technology. It’s also indirectly defining the level of growth in intelligence, security and copying human emotional behaviour. It is mainly used in market research and testing. Many companies require a good and accurate testing method which contributes to their development by providing the necessary insights and drawing the accurate conclusions. Facial expression recognition technology can be developed through various methods. This technology can be developed by using the deep learning with the convolutional neural network or wit

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Sukumaran, Asha, and Thomas Brindha. "Nature-inspired hybrid deep learning for race detection by face shape features." International Journal of Intelligent Computing and Cybernetics 13, no. 3 (2020): 365–88. http://dx.doi.org/10.1108/ijicc-03-2020-0020.

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PurposeThe humans are gifted with the potential of recognizing others by their uniqueness, in addition with more other demographic characteristics such as ethnicity (or race), gender and age, respectively. Over the decades, a vast count of researchers had undergone in the field of psychological, biological and cognitive sciences to explore how the human brain characterizes, perceives and memorizes faces. Moreover, certain computational advancements have been developed to accomplish several insights into this issue.Design/methodology/approachThis paper intends to propose a new race detection mo

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Younis Abdullah, Nawal, Mohammed Talal Ghazal, and Najwan Waisi. "Pedestrian age estimation based on deep learning." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1548. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1548-1555.

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The large-scale distribution of camera networks in the traffic area resulted in the increasing popularity of video surveillance systems. As pedestrian detection and tracking are the critical monitoring targets in traffic surveillance, many studies focus on pedestrian detection algorithms across cameras. This paper addressed the effect of using the age estimation based on deep convolution neural network (CNN) as a convenience for pedestrian monitoring who is crossing at intersections. Two popular deep convolutional neural networks (DCNNs) pre-trained models have been used in this work, which ha

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Zhang, Weiguo, and Chenggang Zhao. "Exposing Face-Swap Images Based on Deep Learning and ELA Detection." Proceedings 46, no. 1 (2019): 29. http://dx.doi.org/10.3390/ecea-5-06684.

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New developments in artificial intelligence (AI) have significantly improved the quality and efficiency in generating fake face images; for example, the face manipulations by DeepFake are so realistic that it is difficult to distinguish their authenticity—either automatically or by humans. In order to enhance the efficiency of distinguishing facial images generated by AI from real facial images, a novel model has been developed based on deep learning and error level analysis (ELA) detection, which is related to entropy and information theory, such as cross-entropy loss function in the final So

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Zhang, Yang, Peihua Lv, and Xiaobo Lu. "A Deep Learning Approach for Face Detection and Location on Highway." IOP Conference Series: Materials Science and Engineering 435 (November 5, 2018): 012004. http://dx.doi.org/10.1088/1757-899x/435/1/012004.

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28

Sen, Sneha, and Khushboo Sawant. "Face mask detection for covid_19 pandemic using pytorch in deep learning." IOP Conference Series: Materials Science and Engineering 1070, no. 1 (2021): 012061. http://dx.doi.org/10.1088/1757-899x/1070/1/012061.

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29

Gonzalez Dondo, Diego, Javier Andres Redolfi, R. Gaston Araguas, and Daiana Garcia. "Application of Deep-Learning Methods to Real Time Face Mask Detection." IEEE Latin America Transactions 19, no. 6 (2021): 994–1001. http://dx.doi.org/10.1109/tla.2021.9451245.

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30

Lu, Zhengqiu, Chunliang Zhou, Xuyang Xuyang, and Weipeng Zhang. "Face Detection and Recognition Method Based on Improved Convolutional Neural Network." International Journal of Circuits, Systems and Signal Processing 15 (July 30, 2021): 774–81. http://dx.doi.org/10.46300/9106.2021.15.85.

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with rapid development of deep learning technology, face recognition based on deep convolutional neural network becomes one of the main research methods. In order to solve the problems of information loss and equal treatment of each element in the input feature graph in the traditional pooling method of convolutional neural network, a face recognition algorithm based on convolutional neural network is proposed in this paper. First, MTCNN algorithm is used to detect the faces and do gray processing, and then a local weighted average pooling method based on local concern strategy is designed and

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Kapil, Divya, Aishwarya Kamtam, Akhil Kedare, and Smita Bharne. "Deep Learning- Based Surveillance System using Face Recognition." ITM Web of Conferences 32 (2020): 03011. http://dx.doi.org/10.1051/itmconf/20203203011.

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Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. T

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Koshy, Ranjana, and Ausif Mahmood. "Enhanced Deep Learning Architectures for Face Liveness Detection for Static and Video Sequences." Entropy 22, no. 10 (2020): 1186. http://dx.doi.org/10.3390/e22101186.

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Face liveness detection is a critical preprocessing step in face recognition for avoiding face spoofing attacks, where an impostor can impersonate a valid user for authentication. While considerable research has been recently done in improving the accuracy of face liveness detection, the best current approaches use a two-step process of first applying non-linear anisotropic diffusion to the incoming image and then using a deep network for final liveness decision. Such an approach is not viable for real-time face liveness detection. We develop two end-to-end real-time solutions where nonlinear

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Park, Yoon Jung, Hyocheol Ro, Nam Kyu Lee, and Tack-Don Han. "Deep-cARe: Projection-Based Home Care Augmented Reality System with Deep Learning for Elderly." Applied Sciences 9, no. 18 (2019): 3897. http://dx.doi.org/10.3390/app9183897.

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Developing innovative and pervasive smart technologies that provide medical support and improve the welfare of the elderly has become increasingly important as populations age. Elderly people frequently experience incidents of discomfort in their daily lives, including the deterioration of cognitive and memory abilities. To provide auxiliary functions and ensure the safety of the elderly in daily living situations, we propose a projection-based augmented reality (PAR) system equipped with a deep-learning module. In this study, we propose three-dimensional space reconstruction of a pervasive PA

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Devi, D. Gayatri. "COVID Safety Measures Alert System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 269–76. http://dx.doi.org/10.22214/ijraset.2021.36288.

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The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection method is wearing a face mask and maintaining social distance in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports Indicate that wearing facemasks and maintaining social distance while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. So we are doing a Project on detect

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Wu, Hua, Shuang Yang, Weihua Xiong, Shanhu Yu,Xinsheng Sun, and Tongqi Wei. "A Review and Quantitative Evaluation of Small Face Detectors in Deep Learning." Electronic Imaging 2020, no. 6 (2020): 48–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.6.iriacv-048.

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Face detection is crucial to computer vision and many similar applications. Past decades have witnessed great progress in solving this problem. Contrary to traditional methods, recently many researchers have proposed a variety of CNN(Convolutional Neural Network) methods and have given out impressive results in diverse ways. Although many comprehensive evaluations or reviews about face detection are available, very few focuses on small face detection strategies. In this paper, we systematically survey some of the prevailing methods; divide them into two categories and compare them qualitativel

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Zhang, Wenhui, Wentong Wang, Shuang Zhao, and Bin Sun. "Gray-Edge-HOG feature based cascaded learning for facial landmark detection." MATEC Web of Conferences 189 (2018): 10023. http://dx.doi.org/10.1051/matecconf/201818910023.

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Compared with the traditional statistical models, such as the active shape model and the active appearance model, the facial feature point localization method based on deep learning has improved in accuracy and speed, but there still exist some problems. First, when the traditional deep neural network model targets a data set containing different face poses, it only performs the preprocessing through the initialized face alignment, and does not consider the regularity of the distribution of the feature points corresponding to the face pose during feature extraction. Secondly, the traditional d

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Jiang, Lin, Jia Chen, Hiroyoshi Todo, Zheng Tang, Sicheng Liu, and Yang Li. "Application of a Fast RCNN Based on Upper and Lower Layers in Face Recognition." Computational Intelligence and Neuroscience 2021 (September 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/9945934.

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With the development of society, deep learning has been widely used in object detection, face recognition, speech recognition, and other fields. Among them, object detection is a popular direction in computer vision and digital image processing, and face detection is a focus of this hot direction. Although face detection technology has gone through a long research stage, it is still considered as one of the more difficult subjects in human feature detection technology. In addition, the face detection technology itself has two sides, imperceptibility and complexity of the environment, and other

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Iqbal, Muhammad Javed, Muhammad Munwar Iqbal, Iftikhar Ahmad, Madini O. Alassafi, Ahmed S. Alfakeeh, and Ahmed Alhomoud. "Real-Time Surveillance Using Deep Learning." Security and Communication Networks 2021 (September 16, 2021): 1–17. http://dx.doi.org/10.1155/2021/6184756.

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It is crucial to ensure proper surveillance for the safety and security of people and their assets. The development of an aerial surveillance system might be very effective in catering to the challenges in surveillance systems. Current systems are expensive and complex. A cost-effective and efficient solution is required, which is easily accessible to anyone with a moderate budget. In aerial surveillance, quadcopters are equipped with state-of-the-art image processing technology that captures detailed photographs of every object underneath. A quadcopter-based solution is proposed to monitor de

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Einy, Sajad, Cemil Oz, and Yahya Dorostkar Navaei. "IoT Cloud-Based Framework for Face Spoofing Detection with Deep Multicolor Feature Learning Model." Journal of Sensors 2021 (August 30, 2021): 1–18. http://dx.doi.org/10.1155/2021/5047808.

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A face-based authentication system has become an important topic in various fields of IoT applications such as identity validation for social care, crime detection, ATM access, computer security, etc. However, these authentication systems are vulnerable to different attacks. Presentation attacks have become a clear threat for facial biometric-based authentication and security applications. To address this issue, we proposed a deep learning approach for face spoofing detection systems in IoT cloud-based environment. The deep learning approach extracted features from multicolor space to obtain m

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Hajiarbabi, Mohammadreza, and Arvin Agah. "Human Skin Detection in Color Images Using Deep Learning." International Journal of Computer Vision and Image Processing 5, no. 2 (2015): 1–13. http://dx.doi.org/10.4018/ijcvip.2015070101.

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Human skin detection is an important and challenging problem in computer vision. Skin detection can be used as the first phase in face detection when using color images. The differences in illumination and ranges of skin colors have made skin detection a challenging task. Gaussian model, rule based methods, and artificial neural networks are methods that have been used for human skin color detection. Deep learning methods are new techniques in learning that have shown improved classification power compared to neural networks. In this paper the authors use deep learning methods in order to enha

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Zheng, Guangyong, and Yuming Xu. "Efficient face detection and tracking in video sequences based on deep learning." Information Sciences 568 (August 2021): 265–85. http://dx.doi.org/10.1016/j.ins.2021.03.027.

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Adhinata, Faisal Dharma, Diovianto Putra Rakhmadani, Merlinda Wibowo, and Akhmad Jayadi. "A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face." JUITA: Jurnal Informatika 9, no. 1 (2021): 115. http://dx.doi.org/10.30595/juita.v9i1.9624.

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The use of masks on the face in public places is an obligation for everyone because of the Covid-19 pandemic, which claims victims. Indonesia made 3M policies, one of which is to use masks to prevent coronavirus transmission. Currently, several researchers have developed a masked or non-masked face detection system. One of them is using deep learning techniques to classify a masked or non-masked face. Previous research used the MobileNetV2 transfer learning model, which resulted in an F-Measure value below 0.9. Of course, this result made the detection system not accurate enough. In this resea

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Dang, L., Syed Hassan, Suhyeon Im, Jaecheol Lee, Sujin Lee, and Hyeonjoon Moon. "Deep Learning Based Computer Generated Face Identification Using Convolutional Neural Network." Applied Sciences 8, no. 12 (2018): 2610. http://dx.doi.org/10.3390/app8122610.

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Generative adversarial networks (GANs) describe an emerging generative model which has made impressive progress in the last few years in generating photorealistic facial images. As the result, it has become more and more difficult to differentiate between computer-generated and real face images, even with the human’s eyes. If the generated images are used with the intent to mislead and deceive readers, it would probably cause severe ethical, moral, and legal issues. Moreover, it is challenging to collect a dataset for computer-generated face identification that is large enough for research pur

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Schofield, Daniel, Arsha Nagrani, Andrew Zisserman, et al. "Chimpanzee face recognition from videos in the wild using deep learning." Science Advances 5, no. 9 (2019): eaaw0736. http://dx.doi.org/10.1126/sciadv.aaw0736.

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Video recording is now ubiquitous in the study of animal behavior, but its analysis on a large scale is prohibited by the time and resources needed to manually process large volumes of data. We present a deep convolutional neural network (CNN) approach that provides a fully automated pipeline for face detection, tracking, and recognition of wild chimpanzees from long-term video records. In a 14-year dataset yielding 10 million face images from 23 individuals over 50 hours of footage, we obtained an overall accuracy of 92.5% for identity recognition and 96.2% for sex recognition. Using the iden

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Varshney, Neeraj. "Elderly Fall Detection using Lightweight Convolution Deep Learning Model." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2097–103. http://dx.doi.org/10.17762/turcomat.v12i2.1814.

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Old people, who are living alone at home face serious problem of Falls while moving from one place to another and sometime life threading also. In order to prevent this situation, several fall monitoring systems based on sensor data were proposed. However, there was an issue of misclassification to identify the fall as daily life activities and also routine activity as fall. Towards this end, a deep learning based model is proposed in this paper by using the data of heart rate, BP and sugar level to identify fall along with other daily life activities like walking, running jogging etc. For acc

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Liu, Hongyu, and Bo Lang. "Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey." Applied Sciences 9, no. 20 (2019): 4396. http://dx.doi.org/10.3390/app9204396.

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Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the esse

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Tommandru, Suresh, and Domnic Sandanam. "An Automated Framework for Patient Identification and Verification Using Deep Learning." Revue d'Intelligence Artificielle 34, no. 6 (2020): 709–19. http://dx.doi.org/10.18280/ria.340605.

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Automated patient identification and verification are very important at a medical emergency and when patients are not carrying his/her identity. It is a risk factor that identifying the correct patient identity for doctors to provide medical treatment. The majority of the identification or verification is being done by wristbands, RFID tags, fingerprint, face detection by using handcraft feature-based face recognition systems. A new framework based on robust deep learning model and contrast enhancement is proposed in this paper. In the proposed work, the light illumination problem has been add

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., Nitin. "Smart Attendance Management System using Face Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3007–11. http://dx.doi.org/10.22214/ijraset.2021.35597.

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Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. In human interactions, the face is the most important factor as it contains important information about a person or individual. All humans have the ability to recognise individuals from their faces. Now following system is based on face recognition to maintain the attendance record of students. The daily attendance of students is recorded subject

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Kowalski, Marcin. "A Study on Presentation Attack Detection in Thermal Infrared." Sensors 20, no. 14 (2020): 3988. http://dx.doi.org/10.3390/s20143988.

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Face recognition systems face real challenges from various presentation attacks. New, more sophisticated methods of presentation attacks are becoming more difficult to detect using traditional face recognition systems. Thermal infrared imaging offers specific physical properties that may boost presentation attack detection capabilities. The aim of this paper is to present outcomes of investigations on the detection of various face presentation attacks in thermal infrared in various conditions including thermal heating of masks and various states of subjects. A thorough analysis of presentation

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Yu, Bo, Ian Lane, and Fang Chen. "3D Face Detection via Reconstruction Over Hierarchical Features for Single Face Situations." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 04 (2016): 1655013. http://dx.doi.org/10.1142/s0218001416550132.

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There are multiple challenges in face detection, including illumination conditions and diverse poses of the user. Prior works tend to detect faces by segmentation at pixel level, which are generally not computationally efficient. When people are sitting in the car, which can be regarded as single face situations, most face detectors fail to detect faces under various poses and illumination conditions. In this paper, we propose a simple but efficient approach for single face detection. We train a deep learning model that reconstructs face directly from input image by removing background and syn

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