Abstract From border control to mobile device unlocking applications, the practical utility of biometric system can be seriously compromised due face spoofing attacks. So, face recognition systems require greater attention to combating face spoofing attacks. As, face spoofing attacks can be easily propelled through 3D masks, video replays, and printed photos so we are presented face recognition system using motion and similarity features elimination under spoof attacks against the Replay Attack and Institute of Automation, Chinese Academy of Sciences (CASIA) databases. In this paper a calculative analysis has been done by firstly segmenting the foreground and background regions from the input video using Gaussion Mixture Model and secondly by extracting features i.e., face, eye, and nose and applied 26 image quality assessment parameters on spoof face videos under different illumination lighting conditions. The results attained using Replay Attack and CASIA databases are extremely competitive in discriminating from fake traits with paralleled viz-a-viz other approaches. Different machine learning classifiers and their comparative analysis with existing approaches has been shown.

References

[1] Bakshi A., Gupta S., Gupta A., Tanwar S., and Hsiao K., “3T‐FASDM: Linear Discriminant Analysis‐Based Three‐tier Face Anti‐spoofing Detection Model Using Support Vector Machine,” International Journal of Communication Systems, vol. 33, no. 12, pp. e4441, 2020.

[2] Boia R., Dogaru R., and Florea L., “A Comparison of Several Classifiers for Eye Detection on Emotion Expressing Faces,” in Proceeding of the 4th International Symposium on Electrical and Electronics Engineering, Galati, pp. 1-6, 2013.

[3] Boulkenafet Z., Komulainen J., and Hadid A., “On The Generalization of Color Texture-based Face Anti-Spoofing,” Image and Vision Computing, vol. 77, pp. 1-9, 2018.

[4] Chingovska I. and Dos Anjos A., “On the Use of Client Identity Information for Face Antispoofing,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 787- 796, 2015.

[5] Chetty G. and Wagner M., “Biometric Person Authentication with Liveness Detection Based on Audio-Visual Fusion,” International Journal of Biometrics, vol. 1, no. 4, pp. 463-478, 2009.

[6] Comaniciu D., Ramesh V., and Meer P., “Kernel- Based Object Tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564-577, 2003.

[7] Galbally J., Marcel S., and Fierrez J., “Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition,” IEEE transactions on image processing, vol. 23, no. 2, pp. 710-724, 2013.

[8] Gu Y., Zhan J., Ji Y., Li J., Ren F., and Gao S., “MoSense: An RF-based Motion Detection System Via Off-the-shelf WiFi Devices” IEEE Internet of Things Journal, vol. 4, no. 6, pp. 2326- 2341, 2017.

[9] Jain A., Bolle R., and Pankanti S., Biometrics: Personal Identification in Networked Society, Springer Science and Business Media, 2006.

[10] Ji S. and Ye J., “Generalized Linear Discriminant Analysis: a Unified Framework and Efficient Model Selection,” IEEE Transactions on Neural Networks, vol. 19, no. 10, pp. 1768-1782, 2008.

[11] Jones S. and Capson D., “Two-Stage Classification Using Selective Attention for Fast Face Detection,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, pp. v-465, 2005.

[12] Kagawade V. and Angadi S., “Multi-Directional Local Gradient Descriptor: A New Feature Descriptor for Face Recognition,” Image and Vision Computing, vol. 83, pp. 39-50, 2019.

[13] Kollreider K., Fronthaler H., and Bigun J., “Evaluating Liveness By Face Images and the Structure Tensor,” in Proceeding of the 4th IEEE Workshop on Automatic Identification Advanced Technologies, Buffalo, pp. 75-80, 2005.

[14] Kim Y., Na J., Yoon S., and Yi J., “Masked Fake Face Detection Using Radiance Measurements,” JOSA A, vol. 26, no. 4, pp. 760-766, 2009.

[15] Kim Y., Yoo J., and Choi K., “A Motion and Similarity-based Fake Detection Method for Biometric Face Recognition Systems” IEEE Transactions on Consumer Electronics, vol. 57, no. 2, pp. 756-762, 2011.

[16] Lee K. and Byun H., “A New Face Authentication System for Memory-constrained Devices,” IEEE Transactions on Consumer Electronics, vol. 49, no. 4, pp. 1214-1222, 2003.

[17] Li J., Wang Y., Tan T., and Jain A., “Live Face Detection Based on the Analysis of Fourier Spectra,” in Proceeding of the Biometric Technology for Human Identification, Orlando, pp. 296-303, 2004.

[18] Perikos I., Paraskevas M., and Hatzilygeroudis I., “Facial Expression Recognition Using Adaptive Neuro-fuzzy Inference Systems,” in Proceeding of 758 The International Arab Journal of Information Technology, Vol. 19, No. 5, September 2022 the IEEE/ACIS 17th International Conference on Computer and Information Science, Singapore, pp. 1-6, 2018.

[19] Sato M. and Ishii S., “On-line EM Algorithm for the Normalized Gaussian Network,” Neural computation, vol. 12, no. 2, pp. 407-432, 2000.

[20] See Y., Liew E., and Noor N., “Gabor and Maximum Response Filters with Random Forest Classifier for Face Recognition in the Wild” The International Arab Journal of Information Technology, vol. 18, no. 6, pp. 797-806, 2021.

[21] Singh A., Thakur N., and Sharma A., “A Review of Supervised Machine Learning Algorithms,” in Proceeding of the 3rd International Conference on Computing for Sustainable Global Development, New Delhi, pp. 1310-1315, 2016.

[22] Sofia R. and Sivakumar D., “Developing a System for Trauma Identification Based on the Difference from the Normal Human Emotion with Adaptive Neuro Fuzzy Inference System,” in Proceeding of the International Conference on Communication and Signal Processing, Chennai, pp. 0672-0678, 2019.

[23] Stauffer C. and Grimson W., “Adaptive Background Mixture Models for Real-time Tracking,” in Proceedings of the IEEE Computer Society Conference on Computer Vision And Pattern Recognition, Fort Collins, pp. 246- 252,1999.

[24] Sun L., Pan G., Wu Z., and Lao S., “Blinking- based Live Face Detection Using Conditional Random Fields,” in Proceeding of the International Conference on Biometrics, Seoul, pp. 252-260, 2007.

[25] Toth B., “Biometric Liveness Detection,” Information Security Bulletin, vol. 10, no. 8, pp. 291-297, 2005.

[26] Wang R., Huang T., Stubler P., and Mehrotra R., “Robust Face Recognition Based on Motion Pursuit,” in Proceedings of the IEEE International Conference on Multimedia and Expo, Lausanne, pp. 153-156, 2002.

[27] Wang Y., “An analysis of the Viola-Jones Face Detection Algorithm,” Image Processing Online, vol. 4, pp. 128-148, 2014.

[28] Wei W., Hui Q., Peng C., and Shenyi C., “High Level Feedback for Foreground Detection,” in Proceeding of the IEEE Youth Conference on Information, Computing and Telecommunication, Beijing, pp. 323-326, 2009.

[29] Yemez Y., Kanak A., Erzin E., and Tekalp A., “Multimodal Speaker Identification with Audio- Video Processing,” in Proceedings of the International Conference on Image, Barcelona, pp. III-5, 2003.

[30] Zhang D., Biometric Solutions: for Authentication in an E-World, Springer Science and Business Media, 2012.

[31] Zhang Y., Dubey R., Hua G., and Thing V., “Face Spoofing Video Detection Using Spatio-Temporal Statistical Binary Pattern,” in Proceeding of the TENCON IEEE Region 10 Conference, pp. 0309-0314, 2018.

[32] Zivkovic Z. and Van Der Heijden F., “Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction,” Pattern Recognition Letters, vol. 27, no. 7, pp. 773-780, 2006. Aditya Bakshi received a B.Tech degree in computer science and engineering from Kurukshetra University, Haryana, India, in 2010, an M.Tech degree in computer science and engineering from the YMCA University of Science and Technology, Faridabad, India, in 2012. He is currently pursuing a Ph.D. degree in computer science and engineering from Shri Mata Vaishno Devi University, Jammu and Kashmir, India, and an Assistant Professor in the School of Computer Science and Engineering, Lovely Professional University, Punjab, India. He is currently involved in research work on biometric security and manet applications. His research interests include the security of next-generation biometric systems using image processing. Mr.Bakshi is a member of the International Association of Engineers and the Universal Association of Computer and Electronics Engineers. Sunanda Gupta received the Bachelor’s degree in Sciences and Master’s degree in Computer Applications from the University of Jammu, and the Ph.D. degree in Computer Science and Engineering from Shri Mata Vaishno Devi University, Jammu and Kashmir, India, in 2014. She is currently an Assistant Professor in the Department of Computer Science & Engineering, Shri Mata Vaishno Devi University, Jammu and Kashmir, India with more than twelve years of teaching experience. She has authored several research articles in international journals of repute and presented papers in several international/ national conferences. She has also been invited as an expert to various international conferences as a reviewer/ technical program committee member. Her research interests include combinational optimization problems, genetic algorithms, and image processing.