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Relevant bibliographies by topics / Face spoof detection

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Academic literature on the topic 'Face spoof detection'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Face spoof detection"

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Ms., Dilna e. p1, Maneesha Manoj2 Ms., Jiji c. j3 Ms., Jeena c. j. Ms., and Hrudhya k. p5 Ms. "FAKE FACE IDENTIFICATION." International Journal of Advances in Engineering & Scientific Research 4, no. 1 (2017): 40–48. https://doi.org/10.5281/zenodo.10774726.

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<strong>Abstract: </strong>   <strong>Objective-</strong> Automatic face recognition is now widely used in applications ranging from de-duplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose a

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Ms., Dilna e. p1 Ms. Maneesha Manoj2 Ms. Jiji c. j3 Ms. Jeena c. j. 4. Ms. Hrudhya k. p5. "FAKE FACE IDENTIFICATION." International Journal of Advances in Engineering & Scientific Research, ISSN: 2349 –3607 (Online) , ISSN: 2349 –4824 (Print) Vol.4,, Issue 1, Jan-2017, (2017): pp 40–48. https://doi.org/10.5281/zenodo.242479.

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<strong>Abstract: </strong> <strong>Objective-</strong> Automatic face recognition is now widely used in applications ranging from de-duplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose an efficient a

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Mittal, Abhishek, Pravneet Kaur, and Dr Ashish Oberoi. "Hybrid Algorithm for Face Spoof Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (2022): 1028–37. http://dx.doi.org/10.22214/ijraset.2022.40452.

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Abstract: The face spoof detection is the approach which can detect spoofed face. The face spoof detection methods has various phases which include pre-processing, feature extraction and classification. The classification algorithm can classify into two classes which are spoofed or not spoofed. The KNN approach is used previously with the GLCM algorithm for the face spoof detection which give low accuracy. In this research work, the hybrid classification method is proposed which is the combination of random forest, k nearest neighbour and SVM Classifiers. The simulation outcomes depict that th

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Balamurali, K., S. Chandru, Muhammed Sohail Razvi, and V. Sathiesh Kumar. "Face Spoof Detection Using VGG-Face Architecture." Journal of Physics: Conference Series 1917, no. 1 (2021): 012010. http://dx.doi.org/10.1088/1742-6596/1917/1/012010.

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Akash, Chaudhary, AnkitaSingh, and Km.Yachana. "Anti Spoofing Face Detection with Convolutional Neural Networks Classifier." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 745–50. https://doi.org/10.5281/zenodo.7953326.

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The ability to detect spoofed faces has become a critical concern in various applications, such as face recognition systems, banking, and security measures. Thisresearchpresentsa simple system that can detect whether a facein video stream is spoofed or real using pre-trained models for face detection and anti-spoofing. The system uses a continuous loop to read each frame of the video stream, to assess whether a face image is real or spoof, first detect faces using the pre-trained face detection model, then crop and resize the face image. If the model predicts that the face is fake, the system

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Pal, Lovely, and Renuka Singh. "A Face Spoof Detection using Feature Extraction and SVM." International Journal of Science and Research (IJSR) 11, no. 11 (2022): 629–34. http://dx.doi.org/10.21275/sr221103150851.

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Singh, Km Priyanka, Dr Pushpneel Verma, and Ajay Singh. "Technique of Face Spoof Detection using Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 1435–38. http://dx.doi.org/10.22214/ijraset.2022.46847.

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Abstract: Face detection is one in every of the foremost relevent application of image processing and biometric system. Artificial neural networks (ANN) are utilized in the sphere of image processing and pattern recognition. For the recognition and detection of spoofed and non-spoofed images, face spoof approach was proposed. Earlier presented support vector machine classification model is used for the detection of spoofed or non-spoofed images. within the earlier research, SVM based approach was proposed to detect the face spoof. The face spoof detection approaches involves two stages. The in

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Mittal, Abhishek. "Hybrid Classification for Face Spoof Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1732–39. http://dx.doi.org/10.22214/ijraset.2021.39085.

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Abstract: ML (machine learning) is consisted of a method of recognizing face. This technique is useful for the attendance system. Two sets are generated for testing and training phases in order to segment the image, to extract the features and develop a dataset. An image is considered as a testing set; the training set is contrasted when it is essential to identify an image. An ensemble classifier is implemented to classify the test images as recognized or non-recognized. The ensemble algorithm fails to acquire higher accuracy as it classifies the data in two classes. Thus, GLCM (Grey Level Co

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Tamtama, Gabriel Indra Widi, and I. Kadek Dendy Senapartha. "Fake Face Detection System Using MobileNets Architecture." CESS (Journal of Computer Engineering, System and Science) 8, no. 2 (2023): 329. http://dx.doi.org/10.24114/cess.v8i2.43762.

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Sistem pengenalan wajah merupakan salah satu metode dalam teknik biometric yang menggunakan wajah untuk proses identifikasi atau verifikasi seseorang. Teknologi ini tidak memerlukan kontak fisik seperti verifikasi sidik jari dan diklaim lebih aman karena wajah setiap orang memiliki karakter yang berbeda-beda. Terdapat dua fase utama dalam sistem biometrik wajah, yaitu deteksi wajah palsu Presentation Attack (PA) detektor dan pengenalan wajah (face recognition). Penelitian ini melakukan eksperimen dengan tujuan membangun sebuah model pembelajaran mesin (machine learning) berbasis mobile untuk m

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Kaur, Ramandeep, and P. S. "Techniques of Face Spoof Detection: A Review." International Journal of Computer Applications 164, no. 1 (2017): 29–33. http://dx.doi.org/10.5120/ijca2017913569.

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Book chapters on the topic "Face spoof detection"

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Abdullakutty, Faseela, Eyad Elyan, and Pamela Johnston. "Face Spoof Detection: An Experimental Framework." In Proceedings of the International Neural Networks Society. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80568-5_25.

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Singh, Ravi Pratap, Ratnakar Dash, and Ramesh Kumar Mohapatra. "Face Spoof Detection: The Preprocessing Paradigm." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84062-3_2.

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Kushal, Rajeev Sharma, Manish Kumar, and S. P. S. Chauhan. "Face anti-spoof detection technique: A review." In Artificial Intelligence and Information Technologies. CRC Press, 2024. http://dx.doi.org/10.1201/9781003510833-59.

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Eskandari, Maryam, and Omid Sharifi. "Designing Efficient Spoof Detection Scheme for Face Biometric." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94211-7_46.

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Hegde, Nagaratna P., V. Sireesha, Sriperambuduri Vinay Kumar, Paleti Navya Sri, and Pati Sri Sai Mahitha. "Face Spoof Detection Using Effective Machine Learning Techniques." In Cognitive Science and Technology. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9266-5_44.

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Abdulbaqi, Azmi Shawkat, Nawfal Ahmed Turki, Ahmed J. Obaid, Soumi Dutta, and Ismail Yusuf Panessai. "Spoof Attacks Detection Based on Authentication of Multimodal Biometrics Face-ECG Signals." In Artificial Intelligence for Smart Healthcare. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23602-0_30.

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Patil, Pooja R., and Subhash S. Kulkarni. "A Novel Face Print Spoof Detection Using Color Scatter Measures in HSI Space." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1086-8_43.

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Amuthavalli, S., and C. R. Uma Kumari. "Computational Analysis and Performance Investigation of Convolutional Neural Network-Based Algorithms for Effective Face Spoof Detection." In Proceedings of International Conference on Data Analytics and Insights, ICDAI 2023. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3878-0_41.

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Harshitha K N, Shreeraksha B, Swathi M N. "MACHINE LEARNING-DRIVEN FACE SPOOF DETECTION USING ARTIFICIAL NEURAL NETWORKS." In INFORMATION TECHNOLOGY - BIOINFORMATICS INTERNATIONAL CONFERENCE ON ADVANCE IT, ENGINEERING AND MANAGEMENT SACAIM - 2023, VOLUME – 1. RED UNICORN PUBLISHING, 2020. http://dx.doi.org/10.25215/9358096519.10.

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Malar Dhas, Julia Punitha, Martin Victor K., P. Santhiya, Pallavi Sagar Deshpande, Dillip Narayan Sahu, and Joshuva Arockia Dhanraj. "The Classification Approach for Face Spoof Detection in Artificial Neural Networks Based on IoT Concepts." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. https://doi.org/10.4018/979-8-3373-1032-9.ch020.

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Face spoof detection and authentication systemhas significant role in IoT application that include validation of identity for computer security atm access crime detection and social care. Even though the authentication and classification system are vulnerable to different types of attacks. Presentation attack is a clear threat for facial and biometric based security and authentication applications. The discussed issues can be solved using artificial neural networks for face proof detection in IoT platform. The deep learning approaches for feature extraction in multicolor space is useful for ob

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Conference papers on the topic "Face spoof detection"

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Tahmina, Sayeda, Musfequa Rahman, Syed Md Minhaz Hossain, and Kaushik Deb. "CounterfeitFace: A Convolution Neural Network Approach to Face Spoof Detection." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022069.

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Hussein, Layth, D. Sharanya, Gotte Ranjith Kumar, Prashanth V, and Ramya R. "An Enhanced Face Spoof Detection using ResNet50 Based Cosine Learning Rate." In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC). IEEE, 2024. https://doi.org/10.1109/icdscnc62492.2024.10939883.

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P, Ramakanth Kumar, Pranav V. Jambur, Nidhi Vinayak Kulkarni, et al. "Securing ML Models on Websites: Face Recognition and Spoof Detection via IPFS Blockchain." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915410.

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Patil, Neha D., and Sujata V. Kadam. "Face spoof detection techniques: IDA and PCA." In 2016 Online International Conference on Green Engineering and Technologies (IC-GET). IEEE, 2016. http://dx.doi.org/10.1109/get.2016.7916823.

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Thiruchelvam, Pranavi, Sayanthan Sathiyarasah, Thushaliny Paranthaman, and Rajeetha Thaneeshan. "Design Face Spoof Detection using Deep Learning." In 2023 International Conference on Energy, Power, Environment, Control, and Computing (ICEPECC). IEEE, 2023. http://dx.doi.org/10.1109/icepecc57281.2023.10209524.

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Chen, Chengwei, Yaping Jing, Xuequan Lu, Wang Yuan, and Lizhuang Ma. "Spoof Face Detection Via Semi-Supervised Adversarial Training." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892750.

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Biswas, Bikram Kumar, and Mohammad S. Alam. "Efficient live face detection to counter spoof attack in face recognition systems." In SPIE Defense + Security, edited by David Casasent and Mohammad S. Alam. SPIE, 2015. http://dx.doi.org/10.1117/12.2177975.

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Sun, Zhonglin, Li Sun, and Qingli Li. "Investigation in Spatial-Temporal Domain for Face Spoof Detection." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8461942.

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Yadav, Mayank, and Kunal Gupta. "Novel Technique for Face Spoof Detection in Image Processing." In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2018. http://dx.doi.org/10.1109/iccons.2018.8663141.

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Singh, Ravi Pratap, Siddhartha Shekhar Singh, Ratnakar Dash, Vinita Debayani Mishra, Sudeep Kumar Gochhayat, and Debendra Muduli. "Depth-Integrated CNN Approach for Effective Face Spoof Detection." In 2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU). IEEE, 2024. http://dx.doi.org/10.1109/ic-cgu58078.2024.10530841.

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