Implementation of new technique for Face recognition based on quantum computing
Abstract
In this research, a new technique for measuring facial recognition face similarity based on quantum computing (oracle) has been proposed. This technique is transforming the oracle of Grover's search algorithm into correlation oracle with feeding image. The implementation is carried out on a classical computer with Matlab. The improvement in this work came from combination between quantum Grover's algorithm and classical face recognition algorithm where, the number of steps require in Grover's to find target image is which is exponential improvement as compared with steps for classical case. This reduction of steps reduces the time required to process and the complexity and enables us to increase the amount of information to be processed. The quantum phenomena such as superposition, entanglement and vector space are formulated to represent quantum information and quantum processes on the classical computer. Public available AT and T Laboratories in university of Cambridge database are tested on the proposed algorithm.
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[2] M. A. Nielsen & I. L. Chuang,” Quantum Computation and Quantum Information”, the press syndicate of the University of Cambridge, United Kingdom, 2000.
[3] L. K. Grover, “A fast quantum mechanical algorithm for database search “,STOC, Page 212-219,USA,1996.
[4] A. Y. Vlaso,” Quantum Computations and Images Recognition”, arXiv:quant-ph/9703010 (1997).
[5] R. Schützhold, “Pattern recognition on a quantum computer”,Phy. Rev. A 67(6), 062311 (2003).
[6] Beach, G.,Lomont, C., Cohen, C. ” Quantum Image Processing (QuIP)”, Proc. Appl. Imagery Pattern Recognit, Workshop, 39-44 (2003).
[7] Venegas-Andraca, S.E., Bose, S.,” Storing, processing and retrieving an image using quantum mechanics”,Proc. SPIE Conf. Quantum Inf. Comput. vol. 5105, 137–147 (2003).
[8] Venegas-Andraca, S.E.,” Discrete Quantum Walks and Quantum Image Processing”, Thesis submitted for the degree of Doctor of Philosophy at the University of Oxford (2005).
[9] Venegas-Andraca, S.E., Ball, J.L.,” Processing images in entangled quantum systems” ,Quantum Information Processing. 9 (1), 1-11 (2010).
[10] Latorre, J.I.,” Image compression and entanglement”, arXiv:quant-ph/0510031 (2005).
[11] Le, P.Q., Dong, F., Hirota, K., ” A flexible representation of quantum images for polynomial preparation, image compression, and processing operations”, Quantum Inf. Process.10(1),63-84(2011).
[12] Le, P.Q., Iliyasu, A.M., Dong, F., Hirota, K.,” Efficient color transformations on quantum images”, J.Adv. Comput. Intell. Intell. Inf. 15(6), 698-706(2011).
[13]M. Mastriani,”Quantum image processing”,DLQSLLC , 4431NW63RD Drive , Coconut Creek, FL 33073,USA.
[14] Le, P.Q., Iliyasu, A.M., Dong, F.Y., Hirota, K., ”Strategies for designing geometric transformations on quantum images”, Theoretical Computer Science.412(15),1506-1418(2011).
[15] Srivastava, M., Panigrah, P.K.,” Quantum Image Representation Through Two-Dimensional Quantum States and Normalized Amplitude”, arXiv: quant-ph/1305.2251(2013).
[16] Fei Yan , Abdullah M. Iliyasu and Zhengang Jiang ,:( Quantum Computation-Based Image Representation, Processing Operations and Their Applications), Entropy 2014, 16, 5290-5338; doi: 10.3390/e16105290.
[17] AT&T Laboratories Cambridge, The database of faces at http://www.cl.cam.ac.uk/research/dtg/attarchive/facesataglance.html.
