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Iris Print Biometric Identification Using Perceptual Image Hashing Algorithms
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In this paper, we present two hashing based techniques for fast palmprint identification. We first propose three properties required by a hash function and then introduce our first fast identification method based on orientation pattern (OP) hashing. We give the definition of OP and demonstrate that it meets all the requirements of the hash function, and thus, would be appropriate for hashing based fast palmprint identification. We then introduce the second fast identification method based on principal orientation pattern (POP) hashing. Because the POPs are constructed using more stable orientation features, POP hashing can find the target template more quickly thus causing earlier termination of the identification process. We evaluate our methods on the Hong Kong PolyU large-scale database (9667 palms) and the CASIA palmprint database (600 palms) plus a synthetic database (100 000 palms). Experimental results show that, on the Hong Kong PolyU large-scale database, the speedups of OP hashing and POP hashing over brute-force search are 16.93 and 19.91, respectively, and the identification accuracy is slightly higher. While on the CASIA database plus the synthetic database, the speedups of OP hashing and POP hashing are 8.03 and 15.67, respectively, and the identification accuracy almost remains the same. Results also show that, in terms of accuracy, our methods are comparable to several state-of-the-art palmprint identification approaches, while in terms of speed, our methods are much faster.
We investigate the properties of a metric between two distributions, the Earth Mover's Distance (EMD), for content-based image retrieval. The EMD is based on the minimal cost that must be paid to transform one distribution into the other, in a precise sense, and was first proposed for certain vision problems by Peleg, Werman, and Rom. For image retrieval, we combine this idea with a representation scheme for distributions that is based on vector quantization. This combination leads to an image comparison framework that often accounts for perceptual similarity better than other previously proposed methods. The EMD is based on a solution to the transportation problem from linear optimization, for which efficient algorithms are available, and also allows naturally for partial matching. It is more robust than histogram matching techniques, in that it can operate on variable-length representations of the distributions that avoid quantization and other binning problems typical of histograms. When used to compare distributions with the same overall mass, the EMD is a true metric. In this paper we focus on applications to color and texture, and we compare the retrieval performance of the EMD with that of other distances.
A novel biometric identification approach based on the human iris
pattern is proposed. The main idea of this technique is to represent the
features of the iris by fine-to-coarse approximations at different
resolution levels based on the discrete dyadic wavelet transform
zero-crossing representation. The resulting one-dimensional (1D) signals
are compared with model features using different distances. Before
performing the feature extraction, a pre-processing step is to be made
by image processing techniques, isolating the iris and enhancing the
area of study. The proposed technique is translation, rotation and scale
invariant. Results show a classification success above 98%, achieving an
equal error rate equal to 0.21% and the possibility of having null false
acceptance rates with low false rejection rates
This Lecture book is about objective image quality assessment - where the aim is to provide computational models that can automatically predict perceptual image quality. The early years of the 21st century have witnessed a tremendous growth in the use of digital images as a means for representing and communicating information. A considerable percentage of this literature is devoted to methods for improving the appearance of images, or for maintaining the appearance of images that are processed. Nevertheless, the quality of digital images, processed or otherwise, is rarely perfect. Images are subject to distortions during acquisition, compression, transmission, processing, and reproduction. To maintain, control, and enhance the quality of images, it is important for image acquisition, management, communication, and processing systems to be able to identify and quantify image quality degradations. The goals of this book are as follows; a) to introduce the fundamentals of image quality assessment, and to explain the relevant engineering problems, b) to give a broad treatment of the current state-of-the-art in image quality assessment, by describing leading algorithms that address these engineering problems, and c) to provide new directions for future research, by introducing recent models and paradigms that significantly differ from those used in the past. The book is written to be accessible to university students curious about the state-of-the-art of image quality assessment, expert industrial R&D engineers seeking to implement image/video quality assessment systems for specific applications, and academic theorists interested in developing new algorithms for image quality assessment or using existing algorithms to design or optimize other image processing applications.
Algorithms first described in 1993 for recognizing persons by their iris patterns have now been tested in several public field trials, producing no false matches in several million comparison tests. The underlying recognition principle is the failure of a test of statistical independence on texture phase structure as encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 244 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm2 over the iris, enabling real-time decisions about personal identity with extremely high confidence. This paper reviews the current algorithms and presents the results of 2.3 million comparisons among eye images acquired in trials in Britain, the USA, and Japan, and it discusses aspects of the process still in need of improvement.
Proposes a method for personal identification based on iris recognition. The method consists of three major components: image preprocessing, feature extraction and classifier design. A bank of circular symmetric filters is used to capture local iris characteristics to form a fixed length feature vector. In iris matching, an efficient approach called nearest feature line (NFL) is used. Constraints are imposed on the original NFL method to improve performance. Experimental results show that the proposed method has an encouraging performance.
A method for rapid visual recognition of personal identity is
described, based on the failure of a statistical test of independence.
The most unique phenotypic feature visible in a person's face is the
detailed texture of each eye's iris. The visible texture of a person's
iris in a real-time video image is encoded into a compact sequence of
multi-scale quadrature 2-D Gabor wavelet coefficients, whose
most-significant bits comprise a 256-byte “iris code”.
Statistical decision theory generates identification decisions from
Exclusive-OR comparisons of complete iris codes at the rate of 4000 per
second, including calculation of decision confidence levels. The
distributions observed empirically in such comparisons imply a
theoretical “cross-over” error rate of one in 131000 when a
decision criterion is adopted that would equalize the false accept and
false reject error rates. In the typical recognition case, given the
mean observed degree of iris code agreement, the decision confidence
levels correspond formally to a conditional false accept probability of
one in about 1031
Comparison of Iris Recognition Algorithms
- M Vasta
- R Singh
- P Gupta
An Introduction to Image Compression
- Satish Kumar
United States Patent
- John G Daugman
How-To: Python Compare Two Images
- Adrian Rosebrock
Person Identification Technique Using Human Iris Recognition
- Christel-Loic Tisse
- Lionel Torres
- Michel Robert