{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T14:54:26Z","timestamp":1773413666071,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,19]],"date-time":"2018-11-19T00:00:00Z","timestamp":1542585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Biometry based authentication and recognition have attracted greater attention due to numerous applications for security-conscious societies, since biometrics brings accurate and consistent identification. Face biometry possesses the merits of low intrusiveness and high precision. Despite the presence of several biometric methods, like iris scan, fingerprints, and hand geometry, the most effective and broadly utilized method is face recognition, because it is reasonable, natural, and non-intrusive. Face recognition is a part of the pattern recognition that is applied for identifying or authenticating a person that is extracted from a digital image or a video automatically. Moreover, current innovations in big data analysis, cloud computing, social networks, and machine learning have allowed for a straightforward understanding of how different challenging issues in face recognition might be solved. Effective face recognition in the enormous data concept is a crucial and challenging task. This study develops an intelligent face recognition framework that recognizes faces through efficient ensemble learning techniques, which are Random Subspace and Voting, in order to improve the performance of biometric systems. Furthermore, several methods including skin color detection, histogram feature extraction, and ensemble learner-based face recognition are presented. The proposed framework, which has a symmetric structure, is found to have high potential for biometrics. Hence, the proposed framework utilizing histogram feature extraction with Random Subspace and Voting ensemble learners have presented their superiority over two different databases as compared with state-of-art face recognition. This proposed method has reached an accuracy of 99.25% with random forest, combined with both ensemble learners on the FERET face database.<\/jats:p>","DOI":"10.3390\/sym10110651","type":"journal-article","created":{"date-parts":[[2018,11,23]],"date-time":"2018-11-23T03:41:31Z","timestamp":1542944491000},"page":"651","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition"],"prefix":"10.3390","volume":"10","author":[{"given":"Mehmet Akif","family":"Yaman","sequence":"first","affiliation":[{"name":"Institute for Analysis and Scientific Computing, Vienna University of Technology, Vienna 1040, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7630-4084","authenticated-orcid":false,"given":"Abdulhamit","family":"Subasi","sequence":"additional","affiliation":[{"name":"College of Engineering, Effat University, Jeddah 21478, Saudi Arabia"}]},{"given":"Frank","family":"Rattay","sequence":"additional","affiliation":[{"name":"Institute for Analysis and Scientific Computing, Vienna University of Technology, Vienna 1040, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.patrec.2015.12.013","article-title":"50 years of biometric research: Accomplishments, challenges, and opportunities","volume":"79","author":"Jain","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jain, A.K., Ross, A.A., and Nandakumar, K. (2011). Introduction to Biometrics, Springer Science & Business Media.","DOI":"10.1007\/978-0-387-77326-1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.jnca.2014.04.008","article-title":"Data mining in mobile ECG based biometric identification","volume":"44","author":"Sidek","year":"2014","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chihaoui, M., Elkefi, A., Bellil, W., and Ben Amar, C. (2016). A survey of 2D face recognition techniques. Computers, 5.","DOI":"10.3390\/computers5040021"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.procs.2015.04.095","article-title":"Cloud based big data analytics framework for face recognition in social networks using machine learning","volume":"50","author":"Vinay","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1007\/s10489-017-0902-7","article-title":"On the complex domain deep machine learning for face recognition","volume":"47","author":"Tripathi","year":"2017","journal-title":"Appl. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1016\/j.neucom.2014.10.035","article-title":"Learning kernel subspace for face recognition","volume":"151","author":"Li","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, J., Mao, Y., Fang, Q., Liang, Z., Yang, F., and Zhan, S. (2015, January 13\u201315). Heterogeneous face recognition based on super resolution reconstruction by adaptive multi-dictionary learning. Proceedings of the 10th Chinese Conference on Biometric Recognition, Tianjin, China.","DOI":"10.1007\/978-3-319-25417-3_18"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s00138-018-0907-1","article-title":"Deep transformation learning for face recognition in the unconstrained scene","volume":"29","author":"Chen","year":"2018","journal-title":"Mach. Vis. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.knosys.2015.09.002","article-title":"A novel decorrelated neural network ensemble algorithm for face recognition","volume":"89","author":"Dai","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s11042-013-1457-1","article-title":"Kernel sparse representation-based classifier ensemble for face recognition","volume":"74","author":"Zhang","year":"2015","journal-title":"Multimed. Tools Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s11063-013-9288-7","article-title":"Face recognition and micro-expression recognition based on discriminant tensor subspace analysis plus extreme learning machine","volume":"39","author":"Wang","year":"2014","journal-title":"Neural Process. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.patcog.2017.04.014","article-title":"Dynamic ensembles of exemplar-svms for still-to-video face recognition","volume":"69","author":"Bashbaghi","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Odone, F., Pontil, M., and Verri, A. (2009). Machine learning techniques for biometrics. Handbook of Remote Biometrics, Springer.","DOI":"10.1007\/978-1-84882-385-3_10"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shen, L., Bai, L., Bardsley, D., and Wang, Y. (2005). Gabor feature selection for face recognition using improved adaboost learning. Advances in Biometric Person Authentication, Springer.","DOI":"10.1007\/11569947_6"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1016\/j.neucom.2010.12.041","article-title":"Face recognition based on extreme learning machine","volume":"74","author":"Zong","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_17","unstructured":"Kremic, E., Subasi, A., and Hajdarevic, K. (2012, January 25\u201328). Face recognition implementation for client server mobile application using PCA. Proceedings of the ITI 2012 34th International Conference on Information Technology Interfaces, Zagreb, Croatia."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.inffus.2014.05.006","article-title":"Partially-supervised learning from facial trajectories for face recognition in video surveillance","volume":"24","author":"Granger","year":"2015","journal-title":"Inf. Fusion"},{"key":"ref_19","unstructured":"Han, S., Meng, Z., Khan, A., and Tong, Y. (2016, January 5\u201310). Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition. Proceedings of the Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_20","first-page":"287","article-title":"Performance of random forest and SVM in face recognition","volume":"13","author":"Kremic","year":"2016","journal-title":"Int. Arab J. Inf. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.neunet.2017.06.013","article-title":"A novel deep learning algorithm for incomplete face recognition: Low-rank-recovery network","volume":"94","author":"Zhao","year":"2017","journal-title":"Neural Netw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.3390\/sym7031475","article-title":"Performance Enhancement of Face Recognition in Smart TV Using Symmetrical Fuzzy-Based Quality Assessment","volume":"3","author":"Kim","year":"2015","journal-title":"Symmetry"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, S.-Y., Yang, S.-H., Chen, Y.-P., and Huang, J.-W. (2017). Face Liveness Detection Based on Skin Blood Flow Analysis. Symmetry, 12.","DOI":"10.3390\/sym9120305"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s11277-018-5377-2","article-title":"Improving deep learning feature with facial texture feature for face recognition","volume":"103","author":"Li","year":"2018","journal-title":"Wirel. Pers. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, J., Sun, W., Liu, N., Chen, Y., Wang, Y., and Han, S. (2018). A novel multimodal biometrics recognition model based on stacked ELM and CCA methods. Symmetry, 10.","DOI":"10.3390\/sym10040096"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sajid, M., Shafique, T., Manzoor, S., Iqbal, F., Talal, H., Samad Qureshi, U., and Riaz, I. (2018). Demographic-assisted age-invariant face recognition and retrieval. Symmetry, 10.","DOI":"10.3390\/sym10050148"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, W., Yang, J., Xiao, J., Li, S., and Zhou, D. (2014, January 27\u201329). Face recognition based on deep learning. Proceedings of the International Conference on Human Centered Computing, Phnom Penh, Cambodia.","DOI":"10.1007\/978-3-319-15554-8_73"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2588","DOI":"10.1016\/j.patcog.2011.03.013","article-title":"Human face recognition based on multidimensional PCA and extreme learning machine","volume":"10\u201311","author":"Mohammed","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Valenti, R., Sebe, N., Gevers, T., and Cohen, I. (2008). Machine learning techniques for face analysis. Machine Learning Techniques for Multimedia, Springer.","DOI":"10.1007\/978-3-540-75171-7_7"},{"key":"ref_30","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Information Science and Statistics."},{"key":"ref_31","unstructured":"Salah, A.A. (2007, January 5\u20138). Insan ve bilgisayarda y\u00fcz tanima. Proceedings of the International Cognitive Neuroscience Symposium, Marmaris, Turkey. Available online: http:\/\/www.academia.edu\/2666478\/%C4%B0NSAN_VE_B%C4%B0LG%C4%B0SAYARDA_Y%C3%9CZ_TANIMA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","first-page":"27","article-title":"Recognition of human face by face recognition system using 3D","volume":"4","author":"Allaam","year":"2010","journal-title":"J. Inf. Commun. Technol."},{"key":"ref_34","unstructured":"Han, J., Pei, J., and Kamber, M. (2011). Data Mining: Concepts and Techniques, Elsevier."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"12489","DOI":"10.3390\/s120912489","article-title":"Classification of fruits using computer vision and a multiclass support vector machine","volume":"12","author":"Zhang","year":"2012","journal-title":"Sensors"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.3390\/e17041795","article-title":"Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM)","volume":"17","author":"Zhang","year":"2015","journal-title":"Entropy"},{"key":"ref_37","unstructured":"Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J. (2016). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Holmes, G., Pfahringer, B., Kirkby, R., Frank, E., and Hall, M. (2002, January 18\u201322). Multiclass alternating decision trees. Proceedings of the European Conference on Machine Learning, Skopje, Macedonia.","DOI":"10.1007\/3-540-36755-1_14"},{"key":"ref_39","first-page":"438","article-title":"Analysis of WEKA data mining algorithm REP Tree, Simple CART and Random Tree for classification of Indian news","volume":"2","author":"Kalmegh","year":"2015","journal-title":"Int. J. Innov. Sci. Eng. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gr\u0105bczewski, K. (2014). Meta-Learning in Decision Tree Induction, Springer.","DOI":"10.1007\/978-3-319-00960-5"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/TPAMI.2006.211","article-title":"Rotation forest: A new classifier ensemble method","volume":"28","author":"Rodriguez","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1504\/IJBET.2014.064651","article-title":"An ensemble approach to diagnose breast cancer using fully complex-valued relaxation neural network classifier","volume":"15","author":"Saraswathi","year":"2014","journal-title":"Int. J. Biomed. Eng. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s100440200011","article-title":"Bagging, boosting and the random subspace method for linear classifiers","volume":"5","author":"Skurichina","year":"2002","journal-title":"Pattern Anal. Appl."},{"key":"ref_46","unstructured":"Alpaydin, E. (2014). Introduction to Machine Learning, MIT Press."},{"key":"ref_47","first-page":"227","article-title":"A robust skin color based face detection algorithm","volume":"6","author":"Singh","year":"2003","journal-title":"J. Appl. Sci Eng."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kim, K.S., Kim, G.Y., and Choi, H.I. (2008, January 28\u201329). Automatic face detection using feature tracker. Proceedings of the International Conference on Convergence and Hybrid Information Technology, Daejeon, Korea.","DOI":"10.1109\/ICHIT.2008.203"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1109\/34.879790","article-title":"The FERET evaluation methodology for face-recognition algorithms","volume":"22","author":"Phillips","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_50","first-page":"567","article-title":"Signal detection theory and {ROC} analysis","volume":"26","author":"Egan","year":"1975","journal-title":"Psychol. Rec."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1038\/scientificamerican1000-82","article-title":"Better decisions through science","volume":"283","author":"Swets","year":"2000","journal-title":"Sci. Am."},{"key":"ref_52","unstructured":"Provost, F.J., and Fawcett, T. (1997, January 14\u201317). Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA, USA."},{"key":"ref_53","unstructured":"Provost, F.J., Fawcett, T., and Kohavi, R. (1998, January 24\u201327). The case against accuracy estimation for comparing induction algorithms. Proceedings of the Fifteenth International Conference on Machine Learning, Madison, WI, USA."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the ROC curve in the evaluation of machine learning algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recognit."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_58","first-page":"360","article-title":"Understanding interobserver agreement: The kappa statistic","volume":"37","author":"Viera","year":"2005","journal-title":"Fam. Med."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.csda.2015.01.007","article-title":"Kappa statistic for clustered physician\u2013patients polytomous data","volume":"87","author":"Yang","year":"2015","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_60","unstructured":"Kepenekci, B. (2001). Face recognition using gabor wavelet transform. [Ph.D. Thesis, The Middle East Technical University]."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.patcog.2018.04.014","article-title":"Deep CNN based binary hash video representations for face retrieval","volume":"81","author":"Dong","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_62","unstructured":"Le, T.H., and Bui, L. (arXiv, 2011). Face recognition based on SVM and 2DPCA, arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"81","DOI":"10.4018\/jaec.2013010105","article-title":"High performance human face recognition using gabor based pseudo hidden Markov model","volume":"4","author":"Kar","year":"2013","journal-title":"Int. J. Appl. Evol. Comput. IJAEC"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Chihaoui, M., Bellil, W., Elkefi, A., and Amar, C.B. (2016, January 21\u201323). Face recognition using HMM-LBP. Proceedings of the International Conference on Hybrid Intelligent Systems, Marrakech, Morocco.","DOI":"10.1007\/978-3-319-27221-4_21"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/11\/651\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:30:38Z","timestamp":1760196638000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/11\/651"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,19]]},"references-count":64,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["sym10110651"],"URL":"https:\/\/doi.org\/10.3390\/sym10110651","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,19]]}}}