Face recognition based on the proximity measure clustering
V.B. Nemirovskiy, A.K. Stoyanov, D.S. Goremykina
Institute of Cybernetics of Tomsk Polytechnic University, Tomsk, Russia
Full text of article: English language.
In this paper problems of featureless face recognition are considered. The recognition is based on clustering the proximity measures between the distributions of brightness clusters cardinality for segmented images. As a proximity measure three types of distances are used in this work: the Euclidean, cosine and Kullback-Leibler distances. Image segmentation and proximity measure clustering are carried out by means of a software model of the recurrent neural network. Results of the experimental studies of the proposed approach are presented.
featureless comparison, clustering, one-dimensional mapping, neuron, Kullback-Leibler distance, image.
Nemirovskiy VB, Stoyanov AK, Goremykina DS. Face recognition based on the proximity measure clustering. Computer Optics 2016; 40(5): 740-745. DOI: 10.18287/2412-6179-2016-40-5-740-745.
- Wang JZ, Li J, Wiederhold G. SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001; 23(9): 947-963.
- Melnichenco А. Methods of search images by visual similarity and detection fuzzy duplicate images [In Russian]. Russian seminar on Information Retrieval Evaluation. ROMIP Proceedings 2009: 108-121.
- Pimenov V. Simple methods for content-based image retrieval [In Russian]. Russian seminar on Information Retrieval Evaluation. ROMIP Proceedings 2010: 69-79.
- Kuharev GA. Biometric systems: Methods and tools for the identification of the human person [In Russian]. Saint-Petersburg: Politechnika; 2001.
- Hemant SM, Harpreet K. Face Recognition using PCA & neural network. International Journal of Emerging Science and Engineering (IJESE) 2013; 1(6): 71-75.
- Varlamov A, Sharapov R. Machine Learning of Visually Similar Images Search. CEUR Workshop Proceedings 2012; 934: 113-120.
- Taigman Y, Yang M, Ranzato M, Wolf L. DeepFace: Closing the Gap to Human-Level Performance in Face Verification. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014: 1701-1708. DOI: 10.1109/CVPR.2014.220.
- Ibragimov VV, Arsentjev DA. Algorithms and methods for person recognition in the conditions of modern information technologies [In Russian]. Bulletin MGUP 2015; 1: 67-69.
- Seredin OS. Linear methods of pattern recognition on the sets of the objects of arbitrary nature represented by pairwise comparisons. The general case [In Russian]. Izvestija TulGU: Natural Sciences 2012; 1: 141-152.
- Seredin OS, Mottl VV, Tatarchuk AI, Razin NA Convex selective criteria of relevant vectors method in the spaceof objects pairwise comparisons [In Russian]. Izvestija TulGU: Natural sciences 2013; 1: 165-176.
- Vorontsov KV. Lectures on the metric classification algorithms [In Russian]. Moscow: MFTI; 2007.
- Nemirovskiy VB, Stoyanov AK. Near-duplicate image recognition based on the rank distribution of the brightness clusters cardinality. Computer Optics 2014; 38(4): 811-817.
- Nemirovskiy VB, Stoyanov AK. Near-duplicate image recognition. Mechanical Engineering, Automation and Control Systems (MEACS). Proceedings of the International Conference, Tomsk, 2014, Institute of Electrical and Electronics Engineers (IEEE) 2014; 4. DOI: 10.1109/MEACS.2014.6986916.
- Nemirovskiy VB, Stoyanov AK. Application of recurrent neural network for multi-step full-color image segmentation. The 8th International Forum on Strategic Technology (IFOST-2013), 2013, Ulaanbaatar, Mongolia. Mongolian University of Science and Technology 2013; 2: 221-224, DOI: 10.1109/IFOST.2013.6616891.
- Nemirovskiy VB, Stoyanov AK. Multi-step segmentation of images by means of a recurrent neural network. The 7th International Forum on Strategic Technology (IFOST-2012), September 18-21, 2012, Tomsk: [proceedings], National Research Tomsk Polytechnic University (TPU), [S. l.]: IEEE, 2012, 4, DOI: 10.1109/IFOST.2012.6357619.
- Nemirovskiy VB, Stoyanov AK. Image segmentation by recurrent neural network. [In Russian]. Bulletin of the Tomsk Polytechnic University 2012; 321(5): 205-210.
- Danilov VI. Lectures on fixed points [In Russian, Electonic Preprint]. Moscow: Russian Economic School; 2006. Source: <https://www.nes.ru/dataupload/files/programs/econ/preprints/2006/danilov_fixed_points.pdf>.
- Collection of Facial Images. Source: <http://cswww.essex.ac.uk/mv/allfaces/index.html/>.
© 2009, IPSI RAS
Institution of Russian Academy of Sciences, Image Processing Systems Institute of RAS, Russia, 443001, Samara, Molodogvardeyskaya Street 151; e-mail: email@example.com; Phones: +7 (846) 332-56-22, Fax: +7 (846) 332-56-20