Qiong Li

Bio: Qiong Li is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Quantum key distribution & Key (cryptography). The author has an hindex of 14, co-authored 102 publications receiving 854 citations. Previous affiliations of Qiong Li include Harbin Institute of Technology Shenzhen Graduate School.

A novel quantum representation of color digital images

Abstract: In this paper, we propose a novel quantum representation of color digital images (NCQI) in quantum computer. The freshly proposed quantum image representation uses the basis state of a qubit sequence to store the RGB value of each pixel. All pixels are stored into a normalized superposition state and can be operated simultaneously. Comparison results with the latest multi-channel representation for quantum image reveal that NCQI can achieve a quadratic speedup in quantum image preparation. Meanwhile, some NCQI-based image processing operations are discussed. Analyses and comparisons demonstrate that many color operations can be executed conveniently based on NCQI. Therefore, the proposed NCQI model is more flexible and better suited to carry out color quantum image processing.

Finger-vein Verification Using Gabor Filter and SIFT Feature Matching

Abstract: In this paper, a novel method to verify the infrared finger-vein patterns is proposed for biometric purposes. Firstly, we select parameters for Gabor filter with eight orientations to exploit finger-vein network, then we extract vein patterns by the fusion of two distinct orientation results. Secondly, we utilize SIFT features to offset the effect of images rotation and shift impact during finger-vein verification. Finally, the number of matching SIFT features between the registered and test finger vein patterns is calculated as the similarity measurement to verify the personal identification. The experiment results show that EER is low to 0.46%, which demonstrates our proposed approach is valid and effective for finger-vein verification.

Toward accurate localization and high recognition performance for noisy iris images

Abstract: Iris recognition plays an important role in biometrics. Until now, many scholars have made different efforts in this field. However, the recognition performances of most proposed methods degrade dramatically when the image contains some noise, which inevitably occurs during image acquisition such as reflection spots, inconsistent illumination, eyelid, eyelash, hair, etc. In this paper, an accurate iris localization and high recognition performance approach for noisy iris images is presented. After filling the reflection spots using the inpainting method which is based on Navier-Stokes (NS) equations, the Probable boundary (Pb) edge detection operator is used to detect pupil edge initially, which can eliminate the interference of inconsistent illumination, eyelid, eyelash and hair. Besides, the accurate circle parameters are obtained in delicately to reduce the input space of Hough transforms. The iris feature code is constructed based on 1D Log-Gabor filter. Our thorough experimental results on the challenging iris image database CASIA-Iris-Thousand achieve an EER of 1.8272 %, which outperforms the state-of-the-art methods.

Perceptually Transparent Information Hiding in G.729 Bitstream

Abstract: In this paper a information hiding algorithm applied to compressed speech bitstream is proposed. Covering codes are used to hide information in the least important bits in G.729 frames. The embedding rate is fairly high compared with the state of art algorithms, while the experiment results show that the distortion of the speech signal is imperceptible.

A review on genetic algorithm: past, present, and future

Abstract: In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

Dual-source discrimination power analysis for multi-instance contactless palmprint recognition

Abstract: Due to the benefits of palmprint recognition and the advantages of biometric fusion systems, it is necessary to study multi-source palmprint fusion systems. Unfortunately, the research on multi-instance palmprint feature fusion is absent until now. In this paper, we extract the features of left and right palmprints with two-dimensional discrete cosine transform (2DDCT) to constitute a dual-source space. Normalization is utilized in dual-source space to avoid the disturbance caused by the coefficients with large absolute values. Thus complicated pre-masking is needless and arbitrary removing of discriminative coefficients is avoided. Since more discriminative coefficients can be preserved and retrieved with discrimination power analysis (DPA) from dual-source space, the accuracy performance is improved. The experiments performed on contactless palmprint database confirm that dual-source DPA, which is designed for multi-instance palmprint feature fusion recognition, outperforms single-source DPA.

Convolutional Neural Network for Finger-Vein-Based Biometric Identification

Abstract: The use of human finger-vein traits for the purpose of automatic user recognition has gained a lot of attention in recent years. Current state-of-the-art techniques can provide relatively good performance, yet they are strongly dependent upon the quality of the analyzed finger-vein images. In this paper, we propose a convolutional-neural-network-based finger-vein identification system and investigate the capabilities of the designed network over four publicly available databases. The main purpose of this paper is to propose a deep-learning method for finger-vein identification, which is able to achieve stable and highly accurate performance when dealing with finger-vein images of different quality. The reported extensive set of experiments show that the accuracy achievable with the proposed approach can go beyond 95% correct identification rate for all the four considered publicly available databases.

A Survey of Iris Biometrics Research: 2008–2010

Abstract: A recent survey of iris biometric research from its inception through 2007, roughly 15 years of research, lists approximately 180 publications. This new survey is intended to update the previous one, and covers iris biometrics research over the period of roughly 2008–2010. Research in iris biometrics has expanded so much that, although covering only 3 years and intentionally being selective about coverage, this new survey lists a larger number of references than the inception-through-2007 survey.