Multi-agent system for selecting images based on the gender and age

Álvaro MARTÍN, David TREJO, Alejandro YAGÜE, José SÁNCHEZ

Abstract


This paper presents a multi-agent system that is able to search people on a database of images recognizing patterns of facial features on each person, based on the main features of the face (eyes, nose and mouth). Using that multi-agent architecture, the system can do the work faster applying Fisherfaces algorithm for the face recognition and classification. This technology can be used for several purposes like specific ads in each user group to suit better their interests or search for the age and gender of people that usually go to different places like malls or shops.


Keywords


Multi-Agent; Facial Recognition; Artificial Vision; Biometrics

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References


Belhumeur, P.N., Hespanha, J.P., and Kriegman, D.J., 1997. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19 (7), 711-720.

Brooks, R.A., 1986. A Roboust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation RA-2, 14-23.

Chamoso, Pablo, Pérez-Ramos, Henar, and García-García, Ángel, 2014. Altair: Supervised Methodology to Obtain Retinal Vessels Caliber. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, Salamanca, v. 3, n. 4, p. 48-57, dec. ISSN 2255-2863.

Corchado, J. M., Bajo, J., De Paz, Y., and Tapia, D. I., 2008. Intelligent environment for monitoring Alzheimer patients, agent technology for health care. Decision Support Systems, 44(2):382-396.

Corchado, J. M., Pavón, J., Corchado, E. S., and Castillo, L. F., 2004. Development of CBR-BDI agents: a tourist guide application. In Advances in case-based reasoning, pp. 547-559. Springer.

Debdeep Banerjee, and Yu Kevin, 2018. Robotic Arm-Based Face Recognition Software Test Automation.

Frikha, T., Siala, Y., Louati, M., and Abid, M., 2016. Use of ridgelets, curvelets application for face recognition: Case study: smart identity card. Advanced Technologies for Signal and Image Processing (ATSIP), 2nd International Conference on. IEEE.

Galdámez, Pedro L., and González Arrieta, Angélica, September 2013. Ear Biometrics: A Small Look at the Process of Ear Recognition. International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. Salamanca, Spain, September 11th-13th, Proceedings. Advances i.

Gil, A.B., Rodríguez-González, S., and Corchado, J.M., 2015. Cloud Computing and Multi Agent System to improve Learning Object Paradigm. Interaction Design and Architecture (s) (23), 38-49.

Kas, Mohamed & Merabet, Youssef & Ruichek, Yassine, & Messoussi, Rochdi, 2018. Mixed Neighborhood Topology Cross Decoded Patterns For Image-Based Face Recognition. Expert Systems with Applications. 114. 10.1016/j.eswa.2018.07.035.

López Sánchez, Daniel, and González Arrieta, Angélica, 2016. Preliminary results on nonparametric facial occlusion detection. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, Salamanca, v. 5, n. 1, pp. 51-61, jan. ISSN 2255-2863.

López-Sánchez, Daniel, Corchado, Juan M., and González Arrieta, Angélica, 2017. A CBR system for e cient face recognition under partial occlusion Case-Based Reasoning Research and Development: 25th International Conference, ICCBR 2017, Trondheim, Norway, June 26-28, 2017, Proceedings. Lecture Notes in Computer Science. Volumen 10339, pp. 170-184.

Lu, C.-Y., Min, H., Gui, J., Zhu, L., and Lei, Y.-K., 2013. Face recognition via weighted sparse representation. J. Visual Commun. Image Represent. 24 (2), 111-116.

Robertson, D.J., and Burton, A.M., 2016. Unfamiliar face recognition: Security, surveillance and smartphones. J. Homeland Defense Secur.Inf. Anal. Center 14-21.

Schmidhuber, J., 2014. The Swiss AI Lab IDSIA, Istituto Dalle Molle di Studi sull’Intelligenza Artificiale, University of Lugano & SUPSI, Galleria 2, 6928 Manno-Lugano, Switzerland.

Sirovich L., and Kirby M., 1987. Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A. 4 (3): 519-524.

Xu Weitao, Shen Yiran, Bergmann Neil, and Hu Wen, 2018. Sensor-Assisted Multi-View Face Recognition System on Smart Glass. IEEE Transactions on Mobile Computing (Volume: 17, Issue: 1, Jan. 1 2018): 197-210 (https://ieeexplore.ieee.org/abstract/document/7922583).

Yamaguchi, Naoya; Navarro Cáceres, María; de la Prieta Pintado, Fernando; Matsui, Kenji (2016) Facial Expression Recognition System for User Preference Extraction. Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing. Volume 474, pp. 453-461.




DOI: http://dx.doi.org/10.14201/ADCAIJ2019814954





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