Abstract Research on face recognition has been evolving for decades. There are numerous approaches developed with highly desirable outcomes in constrained environments. In contrast, approaches to face recognition in an unconstrained environment where varied facial posing, occlusion, aging, and image quality still pose vast challenges. Thus, face recognition in the unconstrained environment still an unresolved problem. Many current techniques are not performed well when experimented in unconstrained databases. Additionally, most of the real-world application needs a good face recognition performance in the unconstrained environment. This paper presents a comprehensive process aimed to enhance the performance of face recognition in an unconstrained environment. This paper presents a face recognition system in an unconstrained environment. The fusion between Gabor filters and Maximum Response (MR) filters with Random Forest classifier is implemented in the proposed system. Gabor filters are a hybrid of Gabor magnitude filters and Oriented Gabor Phase Congruency (OGPC) filters. Gabor magnitude filters produce the magnitude response while the OGPC filters produce the phase response of Gabor filters. The MR filters contain the edge- and bar-anisotropic filter responses and isotropic filter responses. In the face features selection process, Monte Carlo Uninformative Variable Elimination Partial Least Squares Regression (MC-UVE-PLSR) is used to select the optimal face features in order to minimize the computational costs without compromising the accuracy of face recognition. Random Forests is used in the classification of the generated feature vectors. The algorithm performance is evaluated using two unconstrained facial image databases: Labelled Faces in the Wild (LFW) and Unconstrained Facial Images (UFI). The proposed technique used produces encouraging results in these evaluated databases in which it recorded face recognition rates that are comparable with other state-of-the-art algorithms.

References

[1] Ahonen T., Hadid A., and Pietikainen M., “Face Description with Local Binary Patterns: Application to Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, 2006.

[2] Arashloo S. and Kittler J., “Efficient Processing of Mrfs for Unconstrained-Pose Face Recognition,” in Proceedings of 6th International Conference on Biometrics: Theory, Applications and Systems, Arlington, pp. 1-8, 2013.

[3] Arashloo S. and Kittler J., “Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 12, pp. 2100-2109, 2014.

[4] Barkan O., Weill J., Wolf L., and Aronowitz H., “Fast High Dimensional Vector Multiplication Face Recognition,” in Proceedings of the IEEE International Conference on Computer Vision, Sydney, pp. 1960-1967, 2013.

[5] Benitez-Garcia G., Nakano-Miyatake M., Olivares-Mercado J., Perez-Meana H., Sanchez- Perez G., and Toscano-Medina K., “A Low Complexity Face Recognition Scheme Based on Down Sampled Local Binary Patterns,” The International Arab Journal of Information Technology, vol. 16, no. 3, pp. 338-347, 2019.

[6] Caenen G. and Gool L., “Maximum Response Filters for Texture Analysis,” in Proceedings of Conference on Computer Vision and Pattern Recognition Workshop, Washington, pp. 58-58, 2004.

[7] Cai W., Li Y., and Shao X., “A Variable Selection Method Based on Uninformative Variable Elimination for Multivariate Calibration of Near-Infrared Spectra,” Chemometrics and Intelligent Laboratory Systems, vol. 90, no. 2, pp. 188-194, 2008.

[8] Chen D., Cao X., Wang L., Wen F., and Sun J., “Bayesian Face Revisited: A Joint Formulation,” in Proceedings of European Conference on Computer Vision, Florence, pp. 566-579, 2012.

[9] Chen D., Cao X., Wen F., and Sun J., “Blessing of Dimensionality: High-Dimensional Feature and its Efficient Compression for Face Verification,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, pp. 3025-3032, 2013. Gabor and Maximum Response Filters with Random Forest Classifier for ... 805

[10] Déniz O., Bueno G., Salido J., and Torre F., “Face Recognition using Histograms of Oriented Gradients,” Pattern Recognition Letters, vol. 32, no. 12, pp. 1598-1603, 2011.

[11] Devi N. and Hemachandran K., “Content based Feature Combination Method for Face Image Retrieval using Neural Network and SVM Classifier for Face Recognition,” Indian Journal of Science and Technology, vol. 10, no. 24, pp. 1- 11, 2017.

[12] Faraji M., Shanbehzadeh J., Nasrollahi K., and Moeslund T., “Extremal Regions Detection Guided by Maxima of Gradient Magnitude,” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5401-5415, 2015.

[13] Geusebroek J., Smeulders A., and Van-De- Weijer J., “Fast Anisotropic Gauss Filtering,” IEEE Transactions on Image Processing, vol. 12, no. 8, pp. 938-943, 2003.

[14] Huang G., Ramesh M., Berg T., and Learned- Miller E., “Labeled Faces in The Wild: A Database for Studying Face Recognition in Unconstrained Environments,” Technical Report, University of Massachusetts, 2007.

[15] Juefei-Xu F., Luu K., and Savvides M., “Spartans: Single-Sample Periocular-Based Alignment-Robust Recognition Technique Applied to Non-Frontal Scenarios,” IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 4780-4795, 2015.

[16] Kovesi P., “Phase Congruency: A Low-Level Image Invariant,” Psychological Research, vol. 64, no. 2, pp. 136-148, 2000.

[17] Král P. and Vrba A., “Enhanced Local Binary Patterns for Automatic Face Recognition,” arXiv preprint arXiv:1702.03349, 2017.

[18] Lenc L. and Král P., “Automatically Detected Feature Positions for LBP Based Face Recognition,” in Proceedings of IFIP International Conference on Artificial Intelligence Applications and Innovations, Rhodos, pp. 246-255, 2014.

[19] Lenc L. and Král P., “Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions,” in Proceedings of Mexican International Conference on Artificial Intelligence, Cuernavaca, pp. 349-361, 2015.

[20] Lenc L. and Král P., “Local Binary Pattern Based Face Recognition with Automatically Detected Fiducial Points,” Integrated Computer-Aided Engineering, vol. 23, no. 2, pp. 129-139, 2016.

[21] Li H. and Hua G., “Hierarchical-PEP Model for Real-World Face Recognition,” in Proceedings of the Conference on Computer Vision and Pattern Recognition, Boston, pp. 4055-4064, 2015.

[22] Li H., Hua G., Shen X., Lin Z., and Brandt J., “Eigen-Pep for Video Face Recognition,” in Proceedings of Asian Conference on Computer Vision, Singapore, pp. 17-33, 2014.

[23] Lin J. and Chiu C., “LBP Edge-Mapped Descriptor Using MGM Interest Points for Face Recognition,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing, New Orleans, pp. 1183-1187, 2017.

[24] Liu W. and Wang Z., “Facial Expression Recognition Based on Fusion of Multiple Gabor Features,” in Proceedings of 18th International Conference on Pattern Recognition, Hong Kong, pp. 536-539, 2006.

[25] Lowe D., “Distinctive Image Features From Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.

[26] Lu C. and Tang X., “Surpassing Human-Level Face Verification Performance on LFW with Gaussian Face,” in Proceedings of 29th AAAI Conference on Artificial Intelligence, pp. 3811- 3819, 2015.

[27] Muruganantham S. and Jebarajan T., “A Comprehensive Review of Significant Researches on Face Recognition Based on Various Conditions,” International Journal of Computer Theory and Engineering, vol. 4, no. 1, pp. 7-15, 2012.

[28] Ojala T., Pietikainen M., and Maenpaa T., “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, 2002.

[29] Pinto N., DiCarlo J., and Cox D., “How Far Can You Get with A Modern Face Recognition Test Set using Only Simple Features?,” in Proceedings of Conference on Computer Vision and Pattern Recognition, Miami, pp. 2591-2598, 2009.

[30] Quah K. and Quek C., “MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections,” IEEE Transactions on Neural Networks, vol. 18, no. 2, pp. 431-448, 2007.

[31] Ruiz-del-Solar J., Verschae R., and Correa M., “Recognition of Faces in Unconstrained Environments: A Comparative Study,” EURASIP Journal on Advances in Signal Processing, vol. 2009, no. 1, pp.1-19, 2009.

[32] Sagonas C., Panagakis Y., Zafeiriou S., and Pantic M., “Robust Statistical Frontalization of Human and Animal Faces,” International journal of Computer Vision, vol. 122, no. 2, pp. 270-291, 2017.

[33] Seo H. and Milanfar P., “Face Verification Using The Lark Representation,” IEEE Transactions on Information Forensics and Security, vol. 6, no. 4, pp. 1275-1286, 2011. 806 The International Arab Journal of Information Technology, Vol. 18, No. 6, November 2021

[34] Sharma G., Hussain ul S., and Jurie F., “Local Higher-Order Statistics (LHS) for Texture Categorization and Facial Analysis,” in Proceedings of European Conference on Computer Vision, Florence, pp. 1-12, 2012.

[35] Shen L. and Bai L., “A Review on Gabor Wavelets for Face Recognition,” Pattern Analysis and Applications, vol. 9, no. 2, pp. 273- 292, 2006.

[36] Simonyan K., Parkhi O., Vedaldi A., and Zisserman A., “Fisher Vector Faces in the Wild,” in Proceedings of British Machine Vision Conference, pp. 1-13, 2013.

[37] Štruc V. and Pavešić N., “Gabor-Based Kernel Partial-Least-Squares Discrimination Features For Face Recognition,” Informatica, vol. 20, no. 1, pp. 115-138, 2009.

[38] Štruc V. and Pavešić N., “The Complete Gabor- Fisher Classifier for Robust Face Recognition,” EURASIP Journal on Advances in Signal Processing, vol. 2010, no. 1, pp. 1-26, 2010.

[39] Sun Y., Wang X., and Tang X., “Hybrid Deep Learning for Face Verification,” in Proceedings of International Conference on Computer Vision, Sydney, pp. 1489-1496, 2013.

[40] Taigman Y., Yang M., Ranzato M., and Wolf L., “Deepface: Closing The Gap to Human-Level Performance in Face Verification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, pp. 1701-1708, 2014.

[41] Tan X. and Triggs B., “Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions,” IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1635-1650, 2010.

[42] Tan Y., Qi J., and Ren F., “Real-Time Cloud Detection in High Resolution Images using Maximum Response Filter and Principle Component Analysis,” in Geoscience and Remote Sensing Symposium, Beijing, pp. 6537- 6540, 2016.

[43] Turhan C. and Bilge H., “Class-Wise Two- Dimensional PCA Method for Face Recognition,” IET Computer Vision, vol. 11, no. 4, pp. 286-300, 2016.

[44] Turk M. and Pentland A., “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.

[45] Vu N., “Exploring Patterns of Gradient Orientations and Magnitudes for Face Recognition,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 2, pp. 295- 304, 2013.

[46] Vu N., Dee H., and Caplier A., “Face Recognition using the POEM Descriptor,” Pattern Recognition, vol. 45, no. 7, pp. 2478- 2488, 2012.

[47] Wolf L., Hassner T., and Taigman Y., “Descriptor Based Methods in the Wild,” in Workshop on Faces In'real-Life'images: Detection, Alignment, and Recognition, 2008.

[48] Xi M., Chen L., Polajnar D., and Tong W., “Local Binary Pattern Network: A Deep Learning Approach for Face Recognition,” in Proceedings of International Conference on Image Processing, Phoenix, pp. 3224-3228, 2016.

[49] Yi D., Lei Z., and Li S., “Towards pose Robust Face Recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, pp. 3539-3545, 2013.

[50] Ylioinas J., Kannala J., Hadid A., and Pietikäinen M., “Face Recognition using Smoothed High- Dimensional Representation,” in Proceedings of Scandinavian Conference on Image Analysis, Copenhagen, pp. 516-529, 2015.

[51] Zhang B., Gao Y., Zhao S., and Liu J., “Local Derivative Pattern Versus Local Binary Pattern: Face Recognition with High-Order Local Pattern Descriptor,” IEEE Transactions on Image Processing, vol. 19, no. 2, pp. 533-544, 2010.

[52] Zhang B., Shan S., Chen X., and Gao W., “Histogram of Gabor Phase Patterns (Hgpp): A Novel Object Representation Approach for Face Recognition,” IEEE Transactions on Image Processing, vol. 16, no. 1, pp. 57-68, 2007. Yuen-Chark See is an Assistant Prorfessor in Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long Campus. He received his PhD from Universiti Teknologi Malaysia. His research interests include machine learning, embedded systems, and wireless sensor networks Eugene Liew received B.Eng in Electrical and Electronic Engineering from Universiti Tunku Abdul Rahman. Norliza Mohd Noor is a Professor in Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia (UTM), Kuala Lumpur Campus. She received her B.Sc. in Electrical Engineering from Texas Tech University in Lubbock, Texas, and Master (by research) and PhD both in Electrical Engineering from UTM. Her research is in machine learning and image analysis for medical and industry applications.