{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:51:05Z","timestamp":1760241065666,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,21]],"date-time":"2019-11-21T00:00:00Z","timestamp":1574294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Optical correlation has a rich history in image recognition applications from a database. In practice, it is simple to implement optically using two lenses or numerically using two Fourier transforms. Even if correlation is a reliable method for image recognition, it may jeopardize decision making according to the location, height, and shape of the correlation peak within the correlation plane. Additionally, correlation is very sensitive to image rotation and scale. To overcome these issues, in this study, we propose a method of nonparametric modelling of the correlation plane. Our method is based on a kernel estimation of the regression function used to classify the individual images in the correlation plane. The basic idea is to improve the decision by taking into consideration the energy shape and distribution in the correlation plane. The method relies on the calculation of the Hausdorff distance between the target correlation plane (of the image to recognize) and the correlation planes obtained from the database (the correlation planes computed from the database images). Our method is tested for a face recognition application using the Pointing Head Pose Image Database (PHPID) database. Overall, the results demonstrate good performances of this method compared to competitive methods in terms of good detection and very low false alarm rates.<\/jats:p>","DOI":"10.3390\/s19235092","type":"journal-article","created":{"date-parts":[[2019,11,22]],"date-time":"2019-11-22T02:49:27Z","timestamp":1574390967000},"page":"5092","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Optical Correlation Decision Performance for Face Recognition by Using a Nonparametric Kernel Smoothing Classification"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7615-707X","authenticated-orcid":false,"given":"Matthieu","family":"Saumard","sequence":"first","affiliation":[{"name":"Yncrea Ouest, Artificial Intelligence and Emerging data Laboratory, 2 rue de la ch\u00e2taigneraie, 35510 Cesson-Sevign\u00e9, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marwa","family":"Elbouz","sequence":"additional","affiliation":[{"name":"Yncrea Ouest, Artificial Intelligence and Emerging data Laboratory, 20 rue du Cuirass\u00e9 Bretagne, 29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Micha\u00ebl","family":"Aron","sequence":"additional","affiliation":[{"name":"Yncrea Ouest, Artificial Intelligence and Emerging data Laboratory, 2 rue de la ch\u00e2taigneraie, 35510 Cesson-Sevign\u00e9, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayman","family":"Alfalou","sequence":"additional","affiliation":[{"name":"Yncrea Ouest, Artificial Intelligence and Emerging data Laboratory, 20 rue du Cuirass\u00e9 Bretagne, 29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Brosseau","sequence":"additional","affiliation":[{"name":"Univ Brest, CNRS, Lab-STICC, 6 avenue Le Gorgeu, 29238 Brest Cedex 3, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Oravec, M. (2010). Understanding correlation techniques for face recognition: From basics to applications. Face Recognition, In-Tech.","DOI":"10.5772\/207"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"067003","DOI":"10.1117\/1.3582861","article-title":"Fuzzy logic and optical correlation-based face recognition method for patient monitoring application in home video surveillance","volume":"50","author":"Elbouz","year":"2011","journal-title":"Opt. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1364\/AO.5.001248","article-title":"A technique for optically convolving two functions","volume":"5","author":"Weaver","year":"1966","journal-title":"Appl. Opt."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/TIT.1964.1053650","article-title":"Signal detection by complex spatial filtering","volume":"10","author":"Lugt","year":"1964","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_5","unstructured":"Goodman, J.W. (1968). Introduction to Fourier Optics, McGraw-Hill."},{"key":"ref_6","unstructured":"Goodfellow, I.J., Bengio, J., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_7","first-page":"607","article-title":"Un filtre de fr\u00e9quences spatial pour l\u2019am\u00e9lioration du contraste des images optiques","volume":"127","author":"Marechale","year":"1953","journal-title":"C. R. Acad. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1364\/AO.23.000812","article-title":"Phase-only matched filtering","volume":"23","author":"Horner","year":"1984","journal-title":"Appl. Opt."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Taouche, C., Batouche, M.C., Chemachema, M., Taleb-Ahmed, A., and Berkane, M. (2014, January 21\u201323). New face recognition method based on local binary pattern histogram. Proceedings of the IEEE International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Hammamet, Tunisia.","DOI":"10.1109\/STA.2014.7086724"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1364\/AO.4.000461","article-title":"Character recognition by incoherent spatial filtering","volume":"4","author":"Armitage","year":"1965","journal-title":"Appl. Opt."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dai-Xian, Z., Zhe, S., and Jing, W. (2015, January 19\u201322). Face recognition method combined with gamma transform and Gabor transform. Proceedings of the IEEE international Conference on Signal Processing, Communications and Computing (ICSPCC), Ningbo, China.","DOI":"10.1109\/ICSPCC.2015.7338828"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"13805","DOI":"10.1007\/s11042-016-3741-3","article-title":"Novel descriptors for geometrical 3D face analysis","volume":"76","author":"Marcolin","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Moos, S., Marcolin, F., Tornincasa, S., Vezzetti, E., Violante, M.G., Fracastoro, G., Speranza, D., and Padula, F. (2017). Cleft lip pathology diagnosis and foetal landmark extraction via 3D geometrical analysis. Int. J. Interact. Design Manuf. (IJIDeM), 11.","DOI":"10.1007\/s12008-014-0244-1"},{"key":"ref_14","unstructured":"Wassermann, L. (2006). All of Nonparametric Statistics, Springer."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tsybakov, A. (2009). Introduction to Nonparametric Estimation, Springer.","DOI":"10.1007\/b13794"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ramsay, J., and Silverman, B.W. (2005). Functional Data Analysis, Springer.","DOI":"10.1007\/b98888"},{"key":"ref_17","unstructured":"Ferraty, F., and Vieu, P. (2006). Nonparametric Functional Data Analysis. Theory and Practice, Springer."},{"key":"ref_18","unstructured":"Gourier, N., Hall, D., and Crowley, J.L. (2004, January 23\u201326). Estimating Face Orientation from Robust Detection of Salient Facial Features. Proceedings of the Pointing 2004, ICPR, International Workshop on Visual Observation of Deictic Gestures, Cambridge, UK."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rucklidge, W. (1996). Efficient Visual Recognition Using the Hausdorff Distance, Springer.","DOI":"10.1007\/BFb0015091"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1109\/34.232073","article-title":"Comparing images using the Hausdorff distance","volume":"15","author":"Huttenlocher","year":"1993","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jesorsky, O., Kirchberg, K.J., and Frischholz, R.W. (2001, January 6\u20138). Robust Face Detection Using the Hausdorff Distance. Proceedings of the Third International Conference on Audio- and Video-based Biometric Person Authentication, Halmstad, Sweden.","DOI":"10.1007\/3-540-45344-X_14"},{"key":"ref_22","unstructured":"Dubuisson, M.-P., and Jain, A.K. (1994, January 9\u201313). A Modified Hausdorff Distance for Object Matching. Proceedings of the 12th International Conference on Pattern Recognition, Jerusalem, Israel."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Devroye, L., Gyorfi, L., and Lugosi, G. (1996). A Probabilistic Theory of Pattern Recognition, Springer.","DOI":"10.1007\/978-1-4612-0711-5"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5092\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:36:21Z","timestamp":1760189781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5092"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,21]]},"references-count":23,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19235092"],"URL":"https:\/\/doi.org\/10.3390\/s19235092","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,11,21]]}}}