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Journal articles on the topic 'Liveness face detection'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

Achmad, Bimo Vallentino, and Supatman Supatman. "FACIAL PHOTO AUTHENTICITY DETECTION USING FACE RECOGNITION AND LIVENESS DETECTION." Jurnal Teknik Informatika (Jutif) 5, no. 5 (2024): 1423–32. https://doi.org/10.52436/1.jutif.2024.5.5.2328.

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Facial recognition has been widely adopted by many systems as authentication. However, relying on facial photos for authentication is insufficient, as these can be manipulated using printed or digital photos. One method that can be used to prevent this is to integrate face recognition with liveness detection. In this research, face recognition and liveness detection are implemented using a Convolutional Neural Network (CNN) because CNN has the ability to process and extract features from photos effectively. There are two types of datasets used, namely CelebA-Spoof for liveness detection and lf

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2

Xin, Yang, Yi Liu, Zhi Liu, et al. "A survey of liveness detection methods for face biometric systems." Sensor Review 37, no. 3 (2017): 346–56. http://dx.doi.org/10.1108/sr-08-2015-0136.

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Purpose Biometric systems are widely used for face recognition. They have rapidly developed in recent years. Compared with other approaches, such as fingerprint recognition, handwriting verification and retinal and iris scanning, face recognition is more straightforward, user friendly and extensively used. The aforementioned approaches, including face recognition, are vulnerable to malicious attacks by impostors; in such cases, face liveness detection comes in handy to ensure both accuracy and robustness. Liveness is an important feature that reflects physiological signs and differentiates art

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3

Enas, A. Raheem, Mumtazah Syed Ahmad Sharifah, and Azizun Wan Adnan Wan. "Insight on face liveness detection: A systematic literature review." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5165–75. https://doi.org/10.11591/ijece.v9i6.pp5165-5175.

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To review researcher"s attempts in response to the problem of spoofing and liveness detection, mapping the research overview from the literature survey into a suitable taxonomy, exploring the basic properties of the field, motivation of using liveness detection methods in face recognition, and Problems that may restrain the advantages. We presented a subjected search on face recognition with liveness detection and its synonyms in four main databases: Web of science, Science Direct, Scopus and IEEE Xplore. We believe that these databases are widely inclusive enough to cover the literature. The

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Wu, Lifang, Yaowen Xu, Meng Jian, Xiao Xu, and Wei Qi. "Face liveness detection scheme with static and dynamic features." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 02 (2018): 1840001. http://dx.doi.org/10.1142/s0219691318400015.

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Face liveness detection is a significant research topic in face-based online authentication. The current face liveness detection approaches utilize either static or dynamic features, but not both. In fact, the dynamic and static features have different advantages in face liveness detection. In this paper, we propose a scheme combining dynamic and static features to capture merits of them for face liveness detection. First, the dynamic maps are captured from the inter-frame motion in the video, which investigates motion information of the face in the video. Then, with a Convolutional Neural Net

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Kim, Sooyeon, Yuseok Ban, and Sangyoun Lee. "Face Liveness Detection Using Defocus." Sensors 15, no. 1 (2015): 1537–63. http://dx.doi.org/10.3390/s150101537.

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6

Raheem, Enas A., Sharifah Mumtazah Syed Ahmad, and Wan Azizun Wan Adnan. "Insight on face liveness detection: A systematic literature review." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5865. http://dx.doi.org/10.11591/ijece.v9i6.pp5865-5175.

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<p>To review researcher’s attempts in response to the problem of spoofing and liveness detection, mapping the research overview from the literature survey into a suitable taxonomy, exploring the basic properties of the field, motivation of using liveness detection methods in face recognition, and Problems that may restrain the advantages. We presented a subjected search on face recognition with liveness detection and its synonyms in four main databases: Web of science, Science Direct, Scopus and IEEE Xplore. We believe that these databases are widely inclusive enough to cover the literat

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7

Shinde, Pratibha, and Ajay Raundale. "Secure Face and Liveness Detection with Criminal Identification for Security Systems." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8s (2023): 497–506. http://dx.doi.org/10.17762/ijritcc.v11i8s.7231.

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The advancement of computer vision, machine learning, and image processing techniques has opened new avenues for enhancing security systems. In this research work focuses on developing a robust and secure framework for face and liveness detection with criminal identification, specifically designed for security systems. Machine learning algorithms and image processing techniques are employed for accurate face detection and liveness verification. Advanced facial recognition methods are utilized for criminal identification. The framework incorporates ML technology to ensure data integrity and ide

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Khairnar, Smita, Shilpa Gite, Ketan Kotecha, and Sudeep D. Thepade. "Face Liveness Detection Using Artificial Intelligence Techniques: A Systematic Literature Review and Future Directions." Big Data and Cognitive Computing 7, no. 1 (2023): 37. http://dx.doi.org/10.3390/bdcc7010037.

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Biometrics has been evolving as an exciting yet challenging area in the last decade. Though face recognition is one of the most promising biometrics techniques, it is vulnerable to spoofing threats. Many researchers focus on face liveness detection to protect biometric authentication systems from spoofing attacks with printed photos, video replays, etc. As a result, it is critical to investigate the current research concerning face liveness detection, to address whether recent advancements can give solutions to mitigate the rising challenges. This research performed a systematic review using t

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9

Singh, Avinash Kumar, Piyush Joshi, and G. C. Nandi. "Face liveness detection through face structure analysis." International Journal of Applied Pattern Recognition 1, no. 4 (2014): 338. http://dx.doi.org/10.1504/ijapr.2014.068327.

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10

Hatture, Sanjeeva Kumar M., and Shweta Policepatil. "Masquerade Attack Analysis for Secured Face Biometric System." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (2021): 225–32. http://dx.doi.org/10.35940/ijrte.b6309.0710221.

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Biometrics systems are mostly used to establish an automated way for validating or recognising a living or nonliving person's identity based on physiological and behavioural features. Now a day’s biometric system has become trend in personal identification for security purpose in various fields like online banking, e-payment, organizations, institutions and so on. Face biometric is the second largest biometric trait used for unique identification while fingerprint is being the first. But face recognition systems are susceptible to spoof attacks made by nonreal faces mainly known as masquerade

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11

Shweta, Policepatil, and Kumar M. Hatture Sanjeeva. "Masquerade Attack Analysis for Secured Face Biometric System." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (2021): 225–32. https://doi.org/10.35940/ijrte.B6309.0710221.

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Biometrics systems are mostly used to establish an automated way for validating or recognising a living or nonliving person's identity based on physiological and behavioural features. Now a day’s biometric system has become trend in personal identification for security purpose in various fields like online banking, e-payment, organizations, institutions and so on. Face biometric is the second largest biometric trait used for unique identification while fingerprint is being the first. But face recognition systems are susceptible to spoof attacks made by nonreal faces mainly known as m

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12

Seo, Jongwoo, and In-Jeong Chung. "Face Liveness Detection Using Thermal Face-CNN with External Knowledge." Symmetry 11, no. 3 (2019): 360. http://dx.doi.org/10.3390/sym11030360.

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Face liveness detection is important for ensuring security. However, because faces are shown in photographs or on a display, it is difficult to detect the real face using the features of the face shape. In this paper, we propose a thermal face-convolutional neural network (Thermal Face-CNN) that knows the external knowledge regarding the fact that the real face temperature of the real person is 36~37 degrees on average. First, we compared the red, green, and blue (RGB) image with the thermal image to identify the data suitable for face liveness detection using a multi-layer neural network (MLP

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13

Albakri, Ghazel, and Sharifa Alghowinem. "The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras." Sensors 19, no. 8 (2019): 1928. http://dx.doi.org/10.3390/s19081928.

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Even though biometric technology increases the security of systems that use it, they are prone to spoof attacks where attempts of fraudulent biometrics are used. To overcome these risks, techniques on detecting liveness of the biometric measure are employed. For example, in systems that utilise face authentication as biometrics, a liveness is assured using an estimation of blood flow, or analysis of quality of the face image. Liveness assurance of the face using real depth technique is rarely used in biometric devices and in the literature, even with the availability of depth datasets. Therefo

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N, Nanthini, Puviarasan N, and Aruna P. "An Efficient Velocity Estimation Approach for Face Liveness Detection using Sparse Optical Flow Technique." Indian Journal of Science and Technology 14, no. 25 (2021): 2128–36. https://doi.org/10.17485/IJST/v14i25.942.

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Abstract <strong>Objectives: </strong>To propose a new liveness detection algorithm using optical flow to ensure the presence of actual live face into a photograph or 2D masks in face recognition biometric security systems. <strong>Methods:</strong> This work proposes an anti-spoofing model namely Sparse Optical Flow Technique with Velocity Estimation Approach (SOFT_VEA). Optical flow is an effective method for tracking objects in motion. It is adapted in this work to capture facial movements and decide the liveness state. The proposed algorithm considers real faces and two kind

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15

Jie, Ooi Zhi, Lim Tong Ming, and Tan Chi Wee. "Biometric Authentication based on Liveness Detection Using Face Landmarks and Deep Learning Model." JOIV : International Journal on Informatics Visualization 7, no. 3-2 (2023): 1057. http://dx.doi.org/10.30630/joiv.7.3-2.2330.

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This paper describes the approach to active liveness detection of the face using facial features and movements. The project aims to create a better method for detecting liveness in real-time on an application programming interface (API) server. The project is built using Python programming with the computer vision libraries OpenCV, dlib and MediaPipe and the deep learning library Tensorflow. There are five modules in active liveness detection progress related to different parts or movements on the face: headshakes, nodding, eye blinks, smiles, and mouths. The functionality of modules runs thro

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Koshy, Ranjana, and Ausif Mahmood. "Optimizing Deep CNN Architectures for Face Liveness Detection." Entropy 21, no. 4 (2019): 423. http://dx.doi.org/10.3390/e21040423.

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Face recognition is a popular and efficient form of biometric authentication used in many software applications. One drawback of this technique is that it is prone to face spoofing attacks, where an impostor can gain access to the system by presenting a photograph of a valid user to the sensor. Thus, face liveness detection is a necessary step before granting authentication to the user. In this paper, we have developed deep architectures for face liveness detection that use a combination of texture analysis and a convolutional neural network (CNN) to classify the captured image as real or fake

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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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18

Singh, Manminder, and A. S. Arora. "A Novel Face Liveness Detection Algorithm with Multiple Liveness Indicators." Wireless Personal Communications 100, no. 4 (2018): 1677–87. http://dx.doi.org/10.1007/s11277-018-5661-1.

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19

Chakraborty, Saptarshi, and Dhrubajyoti Das. "An Overview of Face Liveness Detection." International Journal on Information Theory 3, no. 2 (2014): 11–25. http://dx.doi.org/10.5121/ijit.2014.3202.

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20

Liu, Shuhua, Yu Song, Mengyu Zhang, Jianwei Zhao, Shihao Yang, and Kun Hou. "An Identity Authentication Method Combining Liveness Detection and Face Recognition." Sensors 19, no. 21 (2019): 4733. http://dx.doi.org/10.3390/s19214733.

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In this study, an advanced Kinect sensor was adopted to acquire infrared radiation (IR) images for liveness detection. The proposed liveness detection method based on infrared radiation (IR) images can deal with face spoofs. Face pictures were acquired by a Kinect camera and converted into IR images. Feature extraction and classification were carried out by a deep neural network to distinguish between real individuals and face spoofs. IR images collected by the Kinect camera have depth information. Therefore, the IR pixels from live images have an evident hierarchical structure, while those fr

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21

Wei, Yang, Ivy Kim D. Machica, Cristina E. Dumdumaya, Jan Carlo T. Arroyo, and AllemarJhone P. Delima. "Liveness Detection Based on Improved Convolutional Neural Network for Face Recognition Security." International Journal of Emerging Technology and Advanced Engineering 12, no. 8 (2022): 45–53. http://dx.doi.org/10.46338/ijetae0822_06.

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—Face liveness detection is an important biometric authentication method for face recognition securitythat is used to determine a fake face from an authentic one. In this paper, a liveness detection method based on optimized LeNet5 is proposed. The LeNet-5 is optimized by increasing the convolution kerneland byintroducing a global average pooling. The simulation results show that the proposed model obtained the highest recognition rate of 99.95% as against the 96.67% and 98.23% accuracy from the Support Vector Machine (SVM) and LeNet-5 models, respectively.The results denote that the proposed

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Ryando, Catoer, Riyanto Sigit, Setiawardhana Setiawardhana, and Bima Sena Bayu Dewantara. "Face Recognition for Logging in Using Deep Learning for Liveness Detection on Healthcare Kiosks." JOIV : International Journal on Informatics Visualization 9, no. 1 (2025): 295. https://doi.org/10.62527/joiv.9.1.2759.

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This study explores the enhancement of healthcare kiosks by integrating facial recognition and liveness detection technologies to address the limitations of healthcare service accessibility for a growing population. Healthcare kiosks increase efficiency, lessen the strain on conventional institutions, and promote accessibility. However, there are issues with conventional authentication methods like passwords and RFID, such as the possibility of them being lost, stolen, or hacked, which raises privacy and data security problems. Although it is more secure, face recognition is susceptible to spo

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23

Basurah, Muhammad, Windra Swastika, and Oesman Hendra Kelana. "IMPLEMENTATION OF FACE RECOGNITION AND LIVENESS DETECTION SYSTEM USING TENSORFLOW.JS." Jurnal Informatika Polinema 9, no. 4 (2023): 509–16. http://dx.doi.org/10.33795/jip.v9i4.1332.

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Facial recognition is a popular biometric security system used to authenticate individuals based on their unique facial structure. However, this system is vulnerable to spoofing attacks where the attacker can bypass the system using fake representations of the user's face such as photos, statues or videos. Liveness detection is a method used to address this issue by verifying that the user is a real person and not a representation. This journal article focuses on the life sign method of liveness detection, which utilizes facial movements to confirm the user's existence. We implement the latest

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24

Shinde, Pratibha, and Raundale Ajay R. "Face and liveness detection with criminal identification using machine learning and image processing techniques for security system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 722–29. https://doi.org/10.11591/ijai.v13.i1.pp722-729.

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In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and li

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Suman, Saurabh, and Dr Nagesh Salimath. "Designing Intuitive and Effective Dynamic Facial Authentication: Machine Interaction with Human Factors." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 1642–46. http://dx.doi.org/10.22214/ijraset.2023.54946.

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Abstract: The objective of this paper is to propose a spoof-free Face Liveness Detection system with an active approach that uses the challenge-response methodology which detects the motions and gestures of the user, thereby analyzing and recognizing a real face from a photo. With cybercrimes on the rise each day, identity thefts being one of them- especially in an unsupervised authentication system, has given rise to serious security concerns. Face liveness detection assures the user's actual presence and validates their identities. Along with that, it prevents fraudulent reproduction of face

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Ebrahimpour, Nader, Mustafa Arda Ayden, and Banu Altay. "Liveness control in face recognition with deep learning methods." European Journal of Research and Development 2, no. 2 (2022): 92–101. http://dx.doi.org/10.56038/ejrnd.v2i2.36.

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Today, automatic identification of individuals from biometric features is widely used in identification and authentication, security, and monitoring applications. Since facial recognition is a more user-friendly and comfortable method than other biometric methods, it has grown rapidly in recent years. However, most facial recognition systems are vulnerable to spoofing attacks. Therefore, face liveness detection (FLD) methods are of great importance. On the other hand, unlike traditional methods, deep learning techniques promise to significantly increase the accuracy of facial liveness detectio

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Popereshnyak, S. V., R. O. Skoryk, D. V. Kuptsov, and R. V. Kravchenko. "Human face recognition system in video stream." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2024): 296–304. https://doi.org/10.15407/pp2024.02-03.296.

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In the work, an analysis of detection methods and faces in the video stream and their effectiveness in real time was carried out. Modern algorithms and pre-trained models have been found to be able to recognize faces with high accuracy, but their significant drawback is, in particular, vulnerability to attacks using fake faces. Therefore, the work also analyzed approaches to detecting living faces and the possibility of their implementation in the system. Using an object-oriented approach, a tool for face capture, receiving a video stream from various sources, detecting unknown and previously

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28

Li, Xin, Wei Wu, Tao Li, Yang Su, and Lilin Yang. "Face Liveness Detection Based on Parallel CNN." Journal of Physics: Conference Series 1549 (June 2020): 042069. http://dx.doi.org/10.1088/1742-6596/1549/4/042069.

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29

Pallavi, Gautam, and Jayash Kumar. "Face Liveness Detection using Local Diffused Patterns." International Journal of Computer Applications 149, no. 4 (2016): 1–5. http://dx.doi.org/10.5120/ijca2016911380.

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Kollreider, K., H. Fronthaler, and J. Bigun. "Non-intrusive liveness detection by face images." Image and Vision Computing 27, no. 3 (2009): 233–44. http://dx.doi.org/10.1016/j.imavis.2007.05.004.

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Shinde, Pratibha, and Ajay R. Raundale. "Face and liveness detection with criminal identification using machine learning and image processing techniques for security system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 722. http://dx.doi.org/10.11591/ijai.v13.i1.pp722-729.

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<p>In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinki

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Munirathinam T, Praveen Samuel P M, Prabakaran R, and Jeevanantham S. "Face Recognition with Liveness Detection Login on Flask Web Application." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1321–27. https://doi.org/10.47392/irjaeh.2025.0188.

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Secure user authentication has emerged as a critical issue due to the increased dependence on digital platforms. Conventional password-based systems are still susceptible to attacks including brute-force attacks, phishing, and credential leaks. This project uses Flask to develop a face recognition-based login system with liveness detection in order to overcome these difficulties. The solution integrates liveness detection to prevent spoofing attempts using photos, videos, or masks, and uses deep learning-based facial recognition to confirm user identity. The technology makes sure that only aut

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Yakovlieva, M. A., and Ye R. Kovylin. "The information system for the liveness detection process using aws." System technologies 5, no. 142 (2022): 84–94. http://dx.doi.org/10.34185/1562-9945-5-142-2022-08.

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Analysis of recent studies and publications. The analysis of the modern market of software and algorithmic solutions for performing the Liveness detection process showed that the currently existing approaches are completely commercial solutions with closed algorithms of their work. In addition, the Liveness detection algorithm is not yet standardized, and has many implementation options that can sometimes lead to am-biguous results [7]. That is why, it was decided to develop our own algorithm and liveness detection system based on obtaining face characteristics using the AWS API [8], because t

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Rehman, Yasar Abbas Ur, Lai Man Po, and Mengyang Liu. "LiveNet: Improving features generalization for face liveness detection using convolution neural networks." Expert Systems with Applications 108 (October 2018): 159–69. http://dx.doi.org/10.1016/j.eswa.2018.05.004.

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N, RAMYA. "Liveness Detector for Face Recognition System Fake Vs Real." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47812.

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Abstract - facial recognition systems are increasingly deployed for identity verification and security, but they remain vulnerable to spoofing attacks using photographs, videos, or 3D masks. To address these challenges, a liveness detection mechanism is critical to distinguish between real, live human faces and spoofed or fake inputs. This paper presents a liveness detection framework integrated with a facial recognition system, utilizing techniques such as eye-blink detection, facial micro- movements, texture analysis, and 3D depth estimation. The proposed system aims to enhance the security

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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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PV, Raja Suganya, Sam Joshua S, Vigneshwar B, and Jai Kishan M. "Multi-Level Authentication: Combining Face, Palm, and Liveness Detection for Improved Security." Journal of Innovative Image Processing 5, no. 2 (2023): 181–91. http://dx.doi.org/10.36548/jiip.2023.2.008.

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Face and palm recognition technologies have emerged as powerful tools for authentication, but they can still be vulnerable to fraud and impersonation. Liveness detection is a technique that can detect and prevent fraudulent attempts to bypass authentication by verifying the presence of a live human being during the authentication process. Combining face and palm recognition with liveness detection provides a highly effective and secure approach to authentication, which can prevent fraud and unauthorised access while providing a seamless and user-friendly experience.

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Feng, Litong, Lai-Man Po, Yuming Li, and Fang Yuan. "Face liveness detection using shearlet-based feature descriptors." Journal of Electronic Imaging 25, no. 4 (2016): 043014. http://dx.doi.org/10.1117/1.jei.25.4.043014.

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39

Fernandes, Steven Lawrence, and G. Josemin Bala. "Developing a Novel Technique for Face Liveness Detection." Procedia Computer Science 78 (2016): 241–47. http://dx.doi.org/10.1016/j.procs.2016.02.039.

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Singh, Manminder, and Ajat Shatru Arora. "Computer Aided Face Liveness Detection with Facial Thermography." Wireless Personal Communications 111, no. 4 (2019): 2465–76. http://dx.doi.org/10.1007/s11277-019-06996-6.

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Kim, Sooyeon, Yuseok Ban, and Sangyoun Lee. "Face Liveness Detection Using a Light Field Camera." Sensors 14, no. 12 (2014): 22471–99. http://dx.doi.org/10.3390/s141222471.

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42

Gupta, Sharad. "Anti-Spoofing: Liveness Detection System." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 4997–5003. https://doi.org/10.22214/ijraset.2025.70787.

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Abstract: Face recognition systems became more susceptible to presentation attacks by digital screens, printed images, and 3D masks [3]. This paper introduces a full-fledged anti-spoofing solution based on the YOLO (You Only Look Once) frameworktoidentifyandthwart suchattemptsatspoofinginreal-time[14]. Oursystemintegrates effective object detection features with custom liveness evaluation features to form an effective security layer for biometric authentication systems. Experimental results show high accuracy in distinguishing between real users and spoofing attempts with real-time performance

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Lonkar, Nikita Shrikant. "Banking Security System with Face Liveness Detection Using Machine Learning and Image Processing." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1334–38. https://doi.org/10.22214/ijraset.2025.67510.

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The face is a significant part of the human body, recognizing people in enormous gatherings. Subsequently, on account of its all-inclusiveness and uniqueness, it has turned into the most generally utilized and acknowledged biometric strategy. Biometrics with facial recognition is now widely used. A face identification system should identify not only someone's faces but also detect spoofing attempts with printed face or digital presentations. A sincere spoofing prevention approach is to examine face liveness, such as eye blinking and lips movement. Nevertheless, this approach is helpless when d

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Jagdale, Prasad A., and Sudeep D. Thepade. "Face Liveness Detection using Feature Fusion Using Block Truncation Code Technique." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 8 (2019): 19–22. http://dx.doi.org/10.17762/ijritcc.v7i8.5348.

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Nowadays the system which holds private and confidential data are being protected using biometric password such as finger recognition, voice recognition, eyries and face recognition. Face recognition match the current user face with faces present in the database of that security system and it has one major drawback that it never works better if it doesn’t have liveness detection. These face recognition system can be spoofed using various traits. Spoofing is accessing a system software or data by harming the biometric recognition security system. These biometric systems can be easily attacked b

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Parveen, Sajida, Sharifah Ahmad, Nidaa Abbas, Wan Adnan, Marsyita Hanafi, and Nadeem Naeem. "Face Liveness Detection Using Dynamic Local Ternary Pattern (DLTP)." Computers 5, no. 2 (2016): 10. http://dx.doi.org/10.3390/computers5020010.

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Saini, Meenakshi. "Liveness Detection for Face Recognition in Biometrics: A Review." IOSR Journal of Computer Engineering 02, no. 02 (2016): 31–36. http://dx.doi.org/10.9790/0661-15010020231-36.

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Wang, Shun-Yi, Shih-Hung Yang, Yon-Ping Chen, and Jyun-We Huang. "Face Liveness Detection Based on Skin Blood Flow Analysis." Symmetry 9, no. 12 (2017): 305. http://dx.doi.org/10.3390/sym9120305.

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Hemalatha, S., and Amitabh Wahi. "A Study of Liveness Detection in Face Biometric Systems." International Journal of Computer Applications 91, no. 1 (2014): 31–35. http://dx.doi.org/10.5120/15847-4736.

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Zhu, Xu Guang, Yin Pan Long, Lei Bang Jun, Zou Yao Bin, and Yang Ji Quan. "Eye Region Activity State based Face Liveness Detection System." International Journal of Security and Its Applications 10, no. 1 (2016): 361–74. http://dx.doi.org/10.14257/ijsia.2016.10.1.33.

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Hou, Ya-Li, Xiaoli Hao, Yueyang Wang, and Changqing Guo. "Multispectral face liveness detection method based on gradient features." Optical Engineering 52, no. 11 (2013): 113102. http://dx.doi.org/10.1117/1.oe.52.11.113102.

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