APLICACIÓN DEL CENTRO DE MASA A IMÁGENES DE LA SUPERFICIE DE LOS NUDILLOS PARA GENERACIÓN DE UN VECTOR DE INFORMACIÓN BIOMÉTRICA (APPLICATION OF THE CENTER OF MASS TO IMAGES OF THE FINGER KNUCKLES PRINT TO GENERATE A VECTOR OF BIOMETRIC INFORMATION)
Resumen
La presente investigación está enfocada en analizar y procesar imágenes de la biometría de la parte dorsal de la mano, específicamente en la superficie de los nudillos, ya que pueden servir como elemento biométrico distintivo en comparación con otros rasgos, puesto que es difícil de erosionarse a diferencia de las huellas dactilares, las imágenes en cuestión provienen de la Universidad Politécnica de Hong Kong del Centro de Investigación Biométrica, conjuntamente se realiza el procesamiento digital de señales derivadas de las imágenes al aplicar las variables mecánicas, en específico, el centro de masa, con la finalidad de extraer un total de trece parámetros, para conformar un vector de información el cual que sea capaz de identificar a un individuo de otro.
Palabras Clave: biometría, centro de masa, superficie de los nudillos.
Abstract
The present investigation is focused on analyzing and processing biometric images of the dorsal part of the hand, specifically finger knuckle print, since they can serve as a distinctive biometric element compared to other features, since it is difficult to erode through Unlike fingerprints, the images in question come from the Hong Kong Polytechnic University of the Biometric Research Center, together the digital processing of signals derived from the images is carried out by applying the mechanical variables, specifically, the center of mass, in order to extract a total of thirteen parameters, to form an information vector which is capable of identifying one individual from another.
Keywords: biometrics, center mass, finger knuckle print.
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Shi Y., Yu X., Sohn K., Chandraker M. & Jain A. K., Towards universal representation learning for deep face recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
Kevin A. K. & Cheng H. M., Efficient and accurate 3d finger knuckle matching using surface key points, IEEE Transactions on Image Processing, pp. 8903 - 8915, 2020.
Kumar A. & Kwong C., Towards contactless, low-cost, and accurate 3d fingerprint identification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
Proenca H. & Neves J.C., IRINA: Iris recognition (even) in inaccurately segmented data. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
Summary of NIST Standards for Biometric Accuracy, Temper Resistance, and Interoperability. NIST Report, 2002.
Kekre H.B., Finger-knuckle-print verification using kekre’s wavelet transform. International Conference and Workshop on Emerging Trends in Technology (ICWET 2011), pp. 32 - 38, 2011.
Morales A., Ferrer M. & Travieso C., Improved finger-knuckle-print authentication based on orientation enhancement. Electronics Letters, pp. 380 - 381, 2011.
Shoichiro Aoyama, A finger-knuckle-print recognition algorithm using phase-based. Information Sciences, pp. 53 - 64, 2014.
Kim J., Oh K., Oh B.S., Lin Z. & Toh K.A., A line feature extraction method for finger-knuckle-print verification. Cognitive Computation, 11(1), pp.50-70, 2018.
Jaswal G., Kaul A. & Nath R., Knuckle print biometrics and fusion schemes – overview, challenges, and solutions. ACM Computing Surveys, Vol. 49, No. 2, Article 34, Nov. 2016.
Liu M., Trian Y. & Li L., A new approach for inner-knuckle-print recognition. Journal of Visual Languages and Computing, pp. 33-42, 2014.
Hong L. & Jain A., Fingerprint image enhancement algorithm and performance evaluation. IEEE transactions on pattern analysis and machine intelligence., pp. 777 - 789, 1998.
Otsu, N., A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. Vol. 9, No. 1, 1979, pp. 62–66.
Nalwa V., Automatic on-line signature verification. Proceedings of the IEEE, pp. 223 - 224, 1997.
Rutovitz D., Pattern recognition. Journal of Royal Statistical Society, pp. 504-530, 1966.
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