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Journal of Convergence Information Technology

Iris Recognition Method Based on Quantum Parallelism and Grover Algorithm

Hui Li, Zhanzhan Zhang

Published Feb 28, 2013• 0 citations
Publisher

Abstract

Abstract Traditional iris recognition method has low speed and searching rate under large-scale unstructured database background, it needs N/2 times to find the result when the database has N irises, and the probability of success is 1/2. An improved recognition method based on quantum parallelism and Grover quantum search algorithm is proposed. The feature vector's encoding and Hamming distance are extracted through twice calculations by using quantum parallelism. The Oracle operator in Grover algorithm is improved. The improved Oracle operator includes two databases. The database one contains four registers: index register, quantum bit register which includes target iris Hamming distance, data register, one quantum bit register. Database two includes all the Hamming distance. Compared with traditional recognition method, the proposed method just needs to apply N operations, the target iris location can be found from iris database. The simulation results show that the proposed method decreases the number of repetitions and effectively improves the search speed.

DOI
10.4156/jcit.vol8.issue4.16

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Abstract Traditional iris recognition method has low speed and searching rate under large-scale unstructured database background, it needs N/2 times to find the result when the database has N irises, and the probability of success is 1/2. An improved recognition method based on quantum parallelism and Grover quantum search algorithm is proposed. The feature vector's encoding and Hamming distance are extracted through twice calculations by using quantum parallelism. The Oracle operator in Grover algorithm is improved. The improved Oracle operator includes two databases. The database one contains four registers: index register, quantum bit register which includes target iris Hamming distance, data register, one quantum bit register. Database two includes all the Hamming distance. Compared with traditional recognition method, the proposed method just needs to apply N operations, the target iris location can be found from iris database. The simulation results show that the proposed method decreases the number of repetitions and effectively improves the search speed.
Traditional iris recognition method has low speed and searching rate in large-scale unstructured database. Three different quantum algorithms are proposed in iris recognition. Quantum filtering algorithm can get a better filtering result than classical algorithm in iris filtering process. Quantum Fourier transforms exhibits an exponential speed-up compared with discrete Fourier transform in feature extraction. Improved Grover algorithm can reduce the matching time in the step of iris matching. The experiment results show that matching numbers and matching time reduce greatly.
Traditional fingerprint matching algorithm has slow speed and low probability in search rate under large-scale and unstructured database background. In order to solve this problem, an advanced searching method based on Grover’s search algorithm is proposed. It improves the Oracle operator in Grover’s algorithm and transforms the database one into four registers: index register, quantum bit register which includes target fingerprint matching value, data register, 1 qubit register. Database two is transformed into including all the records of the matched value between input fingerprint and template fingerprints. Compared with traditional algorithm, this improved method can decrease the number of repetitions and raise the probability of success, thus it can effectively improve the search speed and accuracy.
Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT) is used to extract pattern features by only one operation due to quantum parallelism. The proposed algorithm exhibits an exponential speed-up compared with discrete Fourier transform in the feature extraction. Finally, quantum set operations and Grover algorithm are used in palmprint matching. According to the experimental results, quantum algorithm only needs to apply square of N operations to find out the target palmprint, but the traditional method needs N times of calculation. At the same time, the matching accuracy of quantum algorithm is almost 100%.
Grover's quantum algorithm was developed to solve the problem of searching an unstructured database for a certain unique element. In general, this problem can be formulated as follows: an unstructured database consists of elements and contains one unique element that has a certain property that can be tested with polynomial complexity, and which must be found with minimal time and complexity. Classical methods require querying the database to find the desired element, while Grover's algorithm allows you to perform only approximately steps, which are iterations of the procedure, to the database and be sure that the resulting element will be exactly the desired element with a probability close to 1. As in quantum algorithms aimed at cryptanalysis of symmetric transformations, Grover's algorithm repeats the procedure (Grover's iteration) to increase the probability of obtaining the correct result. Similarly to such algorithms in Grover's method, when iterations are continued after reaching the required number of iterations, the probability of obtaining the correct result decreases. This is due to the fact that during the execution of Grover's iteration, a rotation is performed in the complex space. Thus, each iteration, making a turn, brings the register closer and closer to the desired state, but at a certain point a maximum closeness is reached, at which the continued use of iterations will lead to a turn past the desired state, which will move the state of the system away from the desired state. The database for this algorithm can be any search space consisting of a certain number of elements. So, for example, it can be applied to find a secret key for a symmetric cryptograms formation or to find a collision for a hashing function.
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.
Grover′s quantum search algorithm utilizes the parallel properties of quantum mechanics,which has been studied and applied extensively because of its great search efficiency.The principle and performance of Grover′s quantum searching algorithm is researched,especially the quantum algorithm for finding the minimum of a database is deeply researched.Because the search number of the algorithm is high,an improved quantum algorithm which named double threshold for finding a maximum in a database is proposed.The conclusion that the search number of this algorithm is less than the original one through the simulation.The algorithm is applied in multi-user detection,experiment results show that the bit error rate performance of the algorithm is close to the optimal multi-user detection algorithm,and the complexity is less than the optimal one.