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The Scientific World JOURNAL

Research on Palmprint Identification Method Based on Quantum Algorithms

Hui Li, Zhanzhan Zhang

Published Jan 1, 2014• 3 citations
Publisher

Abstract

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%.

DOI
10.1155/2014/670328

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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 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.
Employing the powerful tool of the Quantum Fourier Transform, This paper presents an algorithm of pattern feature selection.Moreover.the proposed algorithm can perform feature selection tasks exponentially faster than its classical counterpart due to quantum parallelism. The features extracted by this algorithm are helpful to the pattern recognition or image recognition.And this paper proved the feasibility of the algorithm through theory deduction and validate serviceability by a simple pattern figure.
Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing.
Quantum image processing uses quantum computing, which implements the concepts of entanglement and superposition to increase efficiency, scalability, and revolutionary promise in demanding image processing ap plications. In this research paper, a comprehensive frame work for quantum image processing (QIP) is presented, which includes innovative quantum algorithms for pattern recognition, image improvement, and representation. The system presents advanced quantum encoding models for effective image data manipulation and storage. In addition, it discusses the quantum image-based algorithms that give better results than classical techniques in terms of speed and accuracy for image enhancement tasks such as edge recognition, contrast adjustment, and noise reduction. Researchers have developed quantum-based feature ex traction and classification methods for pattern recognition, which demonstrate enhanced scalability and accuracy for complex visual data. It has various applications in satellite data processing, secure visual data transmission, and medical imaging highlighted by simulations and theoretical analysis that confirm the proposed approaches.
Creating and using quantum algorithms that might perform better than those of conventional computers is the aim of the emerging field of quantum research. QIP has attracted a lot of interest in the last decade. However, the lack of the quantum feature extraction method limits the interpretation of quantum images. FE is the process of extracting relevant details from raw data. Feature extraction is a crucial component of DIP since it significantly affects the accuracy and efficacy of image identification and classification tasks. Features are essential in the field of image processing. It makes it possible for the content of the photographs to be represented as accurately as possible. Before features are obtained, the image is subjected to a variety of image processing procedures, including scaling, filtering, and grey conversion. The features that will be useful for image recognition and classification are then obtained using feature extraction techniques. The purpose of this study was to demonstrate how well the feature extraction technique can be applied to attain high algorithm performance, which in turn improves performance metrics: mean, skewness, contrast, standard deviation, and root mean square error with 125, 4.60, 0.35, 33, and 0.55. This article explains raw data image preprocessing and the quantum feature technique. The results show how effective and superior the proposed method is. We wrap up by providing an overview of this work, pointing out some important discoveries, and outlining potential directions for further research.
If you read that quantum machine learning applications solve some traditional machine learning problems at an amazing speed, be sure to check if they return quantum results. Quantum outputs, such as magnitude-encoded vectors, limit the use of applications and require additional specifications on how to extract practical and useful results. In satellite images, degradation of image contrast and color quality is a common problem, which brings great difficulties for information extraction and visibility of image objects. This is due to atmospheric conditions, which can cause a loss of color and contrast in the image. Regardless of the band, image enhancement plays a vital role in presenting details more clearly. The research includes new computational algorithms that accelerate multispectral satellite imagery to improve land cover mapping and study feature extraction. The first method is a quantum Fourier transform algorithm based on quantum computing, and the second method is a parallel modified data algorithm based on parallel computing. These calculation algorithms are applied to multispectral images for improvement. The quantum-processed image is compared with the original image, and better results are obtained in terms of visual interpretation of the extracted information. Quantitatively evaluate the performance of the improved method and evaluate the applicability of the improved technology to different land types.