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Indonesian Journal of Electrical Engineering and Computer Science

Research on Iris Recognition Method Based on Quantum Algorithms

Hui Li, Suyan Zhang, Zhanzhan Zhang

Published Sep 1, 2014• 1 citation
Publisher

Abstract

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.

DOI
10.11591/ijeecs.v12.i9.pp6846-6851

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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.
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%.
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.
This work introduces a rapid visual search method for biometric recognition, medical imaging, security monitoring, and multimedia retrieval. Traditional visual search methods involve laborious, unsuccessful pixel-by-pixel comparisons and generated feature descriptors for large datasets. The revolutionary new option of quantum computing uses superposition, entanglement, and parallelism to boost feature discovery, computing similarities, and fine-tuning findings. This paper suggests a fast way to search for images using quantum computing, which uses the Quantum Fourier Transform (QFT) to identify features and quantum similarity measurements to compare images. The suggested solution greatly reduces processing time and improves retrieval accuracy. Performance tests demonstrate quantum computing outperforms classical approaches. Quantum computing has a 25 ms working latency, while standard systems need100−150 ms100-150 ~\text{ms}100−150 ms. With 95% accuracy and scalability, quantum-based search outperforms traditional algorithms. The quantum approach uses 90 joules, while normal systems use 230-280. So, it takes less energy. Quantum computing is helpful and extensible because it handles noise better and uses less memory. Even though quantum computing is difficult to set up, its benefits in computation, parallelism, and real-world usability demonstrate that it could revolutionize visual search technology. Quantum technologies will make large-scale photo retrieval faster and more precise as quantum computing improves.
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.
This article influences the quantum based solutions for traditional pattern matching problem, as the quantum machines can accelerate the computation speed by inherently supporting the parallel executions. The problem identifies solution by suggesting effective algorithms, and as per the classical machines several algorithms are qualified and become criterion for other, importantly noted as Knuth Morris Pratt and Boyer Moore which solves such problem in O(M + N) time. A quantum identical pattern matching algorithms are based upon the principle of high speed Grover's quantum search method which searches an element over unstructured database of "N" elements in O(√N) time. The standard quantum algorithm was proposed by Ramesh and Vinay that could obtain computational speed and provides solution in O(√M + √N) time. We review two already existing quantum based exact and approximate pattern matching algorithms and then by combining the logic of both, a new exact pattern matching algorithm is being proposed which overcomes the algorithmic constraints and substantially proves to be equivalently better than the existing classical and quantum benchmark algorithms. So, this article includes existing and proposed quantum pattern matching algorithms, their flowcharts and examples, mathematical justification over complexity analysis. At last we discuss suitable application domains and further possible variations over the proposed work.