Facial Images Quality Assessment based on ISO/ICAO Standard Compliance Estimation by HMAX Model
الموضوعات :
Azamossadat Nourbakhsh
1
,
محمد شهرام معین
2
,
Arash Sharifi
3
1 - Islamic Azad University
2 -
3 - Islamic Azad University
الکلمات المفتاحية: Facial Images Quality, , ISO/IEC19794 Standard, , ICAO, , FICV, , HMAX Model, ,
ملخص المقالة :
Facial images are the most popular biometrics in automated identification systems. Different methods have been introduced to evaluate the quality of these images. FICV is a common benchmark to evaluate facial images quality using ISO / ICAO compliancy assessment algorithms. In this work, a new model has been introduced based on brain functionality for Facial Image Quality Assessment, using Face Image ISO Compliance Verification (FICV) benchmark. We have used the Hierarchical Max-pooling (HMAX) model for brain functionality simulation and evaluated its performance. Based on the accuracy of compliancy verification, Equal Error Rate of ICAO requirements, has been classified and from those with higher error rate in the past researches, nine ICAO requirements have been used to assess the compliancy of the face images quality to the standard. To evaluate the quality of facial images, first, image patches were generated for key and non-key face components by using Viola-Jones algorithm. For simulating the brain function, HMAX method has been applied to these patches. In the HMAX model, a multi-resolution spatial pooling has been used, which encodes local and public spatial information for generating image discriminative signatures. In the proposed model, the way of storing and fetching information is similar to the function of the brain. For training and testing the model, AR and PUT databases were used. The results has been evaluated by FICV assessment factors, showing lower Equal Error Rate and rejection rate, compared to the existing methods.
[1] ISO/IEC 19794-5, Information technology - Biometric data interchange formats - Part 5: Face image data, 2011.
[2] Y. Wong, Sh. Chen, S. Mau, C. Sanderson, B. C. Lovell ,"Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition ", Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 ,IEEE.
[3] P. Ji, Y. Fang, Zh. Zhou, and J. Zhu, "Fusion of mSSIM and SVM for Reduced-Reference Facial Image Quality Assessment", (Eds.): CCBR 2012, LNCS 7701, pp. 75–82, Springer-Verlag Berlin Heidelberg.
[4] M. Ferrara, A. Franco, D. Maio, "BIOLAB-ICAO: A New Benchmark to Evaluate Applications Assessing Face Image Compliance to ISO/IEC 19794-5 Standard", 978-1-4244-5654-3/09/IEEE ICIP 2009, pp. 41-44. [5] Th. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, “Robust object recognition with cortex-like mechanisms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, 2007, pp. 411–426.
[6] R. Hsu; M. Abdel-Mottaleb, A.K. Jain, "Face detection in color images," IEEE Transactions on Pattern Analysis and Machine Intelligence, May 2002, Vol.24, No.5, pp.696-706.
[7] C. Bouvier, A. Benoit, A. Caplier, P. Y. Coulon “Open Or Closed Mouth State Detection: Static Supervised Classification Based On Log-Polar Signature”, Springer Berlin Heidelberg on Advanced Concepts for Intelligent Vision Systems, 2008, pp. 1093-1102.
[8] W. Qi, Y. Sheng, L. Xianwei, “A Fast Mouth Detection Algorithm Based On Face Organs” IEEE International Conference on Power Electronics and Intelligent Transportation System, December 2009, pp. 250-252.
[9] M. Ferrara, A. Franco, D. Maio, D. Maltoni, "Face Image Conformance to ISO/ICAO Standards in Machine Readable Travel Documents", IEEE Transactions on Information Forensics and Security, AUGUST 2012,Vol 7, No. 4, pp. 1204-1213.
[10] T.H.B Nguyen, V.H. Nguyen, and H. Kim, "Automated conformance testing for ISO/IEC 19794-5 Standard on facial photo specifications", Int. J. Biometrics, Vol. 5, No. 1, 2013, pp.73–98.
[11] S. Coronel Castellanos, I. Solis Moreno, J. A. Cantoral Ceballos, R. Alvarez Vargas, P. L. Martinez Quintal , " An Approach to Improve Mouth-State Detection to Support the ICAO Biometric Standard for Face Image Validation", International Conference on Mechatronics, Electronics and Automotive Engineering, 978-1-4673-8329-5/15 ,2015 IEEE , DOI 10.1109/ICMEAE.2015.12.
[12] R. L. Parente, L. V. Batista, Igor L. P. Andrezza, Erick V. C. L. Borges, Rajiv A. T. Mota, "Assessing Facial Image Accordance to ISO/ICAO Requirements", 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Sao Paulo, Brazil October 2016, IEEE, , DOI: 10.1109/SIBGRAPI.2016.033.
[13] Y. Li, W. Wu, B. Zhang and F. Li, "Enhanced HMAX model with feedforward feature learning for multiclass categorization". Front. Comput. Neurosci, 2015, Vol.9, pp. 123. DOI: 10.3389/fncom.2015.00123.
[14] HH. Gorji, S. Zabbah, R. Ebrahimpour, "A temporal neural network model for object recognition using a biologically plausible decision making layer", 2018, arXiv preprint arXiv:1806.09334.
[15] J. Filali, H. Zghal, J. Martinet. "Ontology and HMAX Features-based Image Classification using Merged Classifiers". International Conference on Computer Vision Theory and Applications2019 (VISAPP’19), Feb 2019, Prague, Czech Republic. hal-02057494.
[16] H. Yu, Z. Xu, Ch. Fu and Y. Wang, "Bottom-up attention based on C1 features of HMAX model", Proc. SPIE 8558, Optoelectronic Imaging and Multimedia Technology II, 85580W (21 November 2012), https://doi.org/10.1117/12.999263.
[17] S. Saraf Esmaili, K. Maghooli & A. Motie Nasrabadi , " A new model for face detection in cluttered backgrounds using saliency map and C2 texture features", International Journal of Computers and Applications, Vol. 40, No. 4, 2018, pp. 214-222.
[18] H.Zh. Zhang, Y.F. Lu, T. K. Kang and M.T. Lim, "B-HMAX: A Fast Binary Biologically Inspired Model for Object Recognition", Neurocomputing, Vol. 218, 19 December 2016, pp. 242-250. http://dx.doi.org/10.1016/j.neucom.2016.08.051.
[19] C. Th´eriault, N. Thome, and M. Cord," Extended coding and pooling in the HMAX model", IEEE Transactions on Image Processing , Vol. 22 , No. 2 , Feb. 2013 , pp. 764 – 777.
[20] E.T Rolls and G Deco, "Computational neuroscience of vision", Press: Oxford, 1st edition, 2006.
[21] J. Mutch and D.G. Lowe, “Object class recognition and localization using sparse features with limited receptive fields,” International Journal of Computer Vision, vol. 80, October 2008, pp. 45–57.
[22] I. Khan, H. Abdullah and M. Shamian Bin Zainal, "Efficient eyes and mouth detection algorithm using combination of viola jones and skin color pixel detection", International Journal of Engineering and Applied Sciences, Vol. 3, No. 4, June 2013, pp. 51-60.
[23] M. Jones and P. Viola., "Face Recognition Using Boosted Local Features". Mitsubishi Electric Research Laboratories Technical Report Number: TR2003-25. Date: April, 2003.
[24] NH. Barnouti, S. Sameer, "Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 5, 2016, pp.371-377.
[25] S. Kaur, A. Chadha, "Supervised Descent Method Viola-Jones and Skin Color Based Face Detection and Tracking ", International Journal of Engineering Development and Research (IJEDR), Volume 5, Issue 2, ISSN: 2321-9939, 2017, pp. 1689-1695.
[26] I. Dagher and H. Al-Bazzaz, "Improving the Component-Based Face Recognition Using Enhanced Viola–Jones and Weighted Voting Technique", Hindawi Modelling and Simulation in Engineering, Volume 2019, Article ID 8234124, 2019, 9 pages. https://doi.org/10.1155/2019/8234124.
[27] Q. Cheng, H. Zhou, and J. Cheng, "The Fisher-Markov Selector: Fast Selecting Maximally Separable Feature Subset for Multiclass Classification with Applications to High-Dimensional Data", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 6, June 2011, pp. 1217-1233.
[28] V.N. Vapnik, "Statistical Learning Theory", Wiley, 1998.
[29] B. Scholkopf and A.J. Smola, "Learning with Kernels", MIT Press, 2002.
[30] M. Riesenhuber and T. Poggio, “Hierarchical models of object recognition in cortex,” Nature Neuroscience, vol. 2, 1999, pp. 1019–1025.
[31] A. Mittal, AK. Moorthy, AC. Bovik, "No-reference image quality assessment in the spatial domain". IEEE Trans. Image Process. Vol. 21, No.
12, 2012, pp. 4695–4708.
[32] A. M. Martinez and R. Benavente, "The AR Face Database", CVC Technical Report No.24, June 1998.
[33] A. Kasinski, A. Florek, A. Schmidt, "The PUT Face Database", Image Processing & Communication. Vol. 13. No. 3, 2008, pp. 59-64.
[34] A. Turpin, F. Scholer, "User performance versus precision measures for simple search tasks", Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Seattle, WA, August 06–11, 2006). New York, NY: ACM. pp. 11–18.
[35] K.H. Brodersen, C.S. Ong, K.E. Stephan, J.M. Buhmann, "The binormal assumption on precision-recall curves ", at the Wayback Machine. December 8, 2012, Proceedings of the 20th International Conference on Pattern Recognition, pp. 4263-4266.
