Iris based identity verification is highly reliable but it can also be subject to attacks. Pupil dilation or constriction stimulated by the application of drugs are examples of sample presentation security attacks which can lead to higher false rejection rates. Suspects on a watch list can potentially circumvent the iris based system using such methods. This paper investigates a new approach using multiple parts of the iris (instances) and multiple iris samples in a sequential decision fusion framework that can yield robust performance. Results are presented and compared with the standard full iris based approach for a number of iris degradations. An advantage of the proposed fusion scheme is that the trade-off between detection errors can be controlled by setting parameters such as the number of instances and the number of samples used in the system. The system can then be operated to match security threat levels. It is shown that for optimal values of these parameters, the fused system also has a lower total error rate.
A new method of biometric identity verification using images of fingers (contact-less sensing) is presented. The method utilizes ridge orientation along lines between easily and reliably extracted key points and bispectral invariant features from the ridge orientation profiles. Rotation is corrected in the pre-processing stage after extraction of key points. Robustness to translation and scale are incorporated in the feature extraction. The method does not rely on minutiae extraction and has potential for feature fusion from multiple fingers for improved performance. A radial basis function Support Vector Machine is trained to perform each identity verification. Results were obtained using 1341 index finger images from 41 individuals with 10-fold cross validation. The system shows about 12% misses at a setting of 1% false alarms and the classification accuracy of the fused system is 92.99%. The performance can be improved with the use of multiple fingers. The proposed methodology can facilitate high traffic, soft identity verification in busy premises such as shopping centres with presentation of the hand as a person walks through.
Higher-order spectral (bispectral and trispectral) analyses of numerical solutions of the Duffing equation with a cubic stiffness are used to isolate the coupling between the triads and quartets, respectively, of nonlinearly interacting Fourier components of the system. The Duffing oscillator follows a period-doubling intermittency catastrophic route to chaos. For period-doubled limit cycles, higher-order spectra indicate that both quadratic and cubic nonlinear interactions are important to the dynamics. However, when the Duffing oscillator becomes chaotic, global behavior of the cubic nonlinearity becomes dominant and quadratic nonlinear interactions are weak, while cubic interactions remain strong. As the nonlinearity of the system is increased, the number of excited Fourier components increases, eventually leading to broad-band power spectra for chaos. The corresponding higher-order spectra indicate that although some individual nonlinear interactions weaken as nonlinearity increases, the number of nonlinearly interacting Fourier modes increases. Trispectra indicate that the cubic interactions gradually evolve from encompassing a few quartets of Fourier components for period-1 motion to encompassing many quartets for chaos. For chaos, all the components within the energetic part of the power spectrum are cubically (but not quadratically) coupled to each other.
The eigenfaces algorithm has long been a mainstay in the field of face recognition due to the high dimensionality of face images. While providing minimal reconstruction error, the eigenface-based transform space de-emphasizes high-frequency information, effectively reducing the information available for classification. Methods such as linear discriminant analysis (also known as fisherfaces) allow the construction of subspaces which preserve the discriminatory information. In this article, multiscale techniques are used to partition the information contained in the frequency domain prior to dimensionality reduction. In this manner, it is possible to increase the information available for classification and, hence, increase the discriminative performance of both eigenfaces and fisherfaces techniques. Motivated by biological systems, Gabor filters are a natural choice for such a partitioning scheme. However, a comprehensive filter bank will dramatically increase the already high dimensionality of extracted features. In this article, a new method for intelligently reducing the dimensionality of Gabor features is presented. The face recognition grand challenge dataset of 3-D face images is used to examine the performance of Gabor filter banks for face recognition and to compare them against other multiscale partitioning methods such as the discrete wavelet transform and the discrete cosine transform.
A new approach to pattern recognition using in- variant parameters based on higher order spectra is presented. In particular, invariant parameters derived from the bispec- trum are used to classify one-dimensional shapes. The bispec- trum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale and amplifi- cation invariant, as well. A minimal set of these invariants is selected as the feature vector for pattern classification, and a minimum distance classifier using a statistical distance measure is used to classify test patterns. The classification technique is shown to distinguish two similar, but different bolts given their one-dimensional profiles. Pattern recognition using higher or- der spectral invariants is fast, suited for parallel implementa- tion, and has high immunity to additive Gaussian noise. Sim- ulation results show very high classification accuracy, even for low signal-to-noise ratios.
Robust image hashing seeks to transform a given input image into a shorter hashed version using a key-dependent non-invertible transform. These image hashes can be used for watermarking, image integrity authentication or image indexing for fast retrieval. This paper introduces a new method of generating image hashes based on extracting Higher Order Spectral features from the Radon projection of an input image. The feature extraction process is non-invertible, non-linear and different hashes can be produced from the same image through the use of random permutations of the input. We show that the transform is robust to typical image transformations such as JPEG compression, noise, scaling, rotation, smoothing and cropping. We evaluate our system using a verification-style framework based on calculating false match, false non-match likelihoods using the publicly available Uncompressed Colour Image database (UCID) of 1320 images. We also compare our results to Swaminathan's Fourier-Mellin based hashing method with at least 1% EER improvement under noise, scaling and sharpening.
An integral part of any audio-visual speech processing (AVSP) system is the front-end visual system that detects facial-features (e.g., eyes and mouth) pertinent to the task of visual speech processing. The ability of this front-end system to not only locate, but also give a confidence measure that the facial-feature is present in the image, directly affects the ability of any subsequent post-processing task such as speech or speaker recognition. With these issues in mind, this paper presents a framework for a facial-feature detection system suitable for use in an AVSP system, but whose basic framework is useful for any application requiring frontal facial-feature detection. A novel approach for facial-feature detection is presented, based on an appearance paradigm. This approach, based on intraclass unsupervised clustering and discriminant analysis, displays improved detection performance over conventional techniques.
Existing asymmetric encryption algorithms require the storage of the secret private key. Stored keys are often protected by poorly selected user passwords that can either be guessed or obtained through brute force attacks. This is a weak link in the overall encryption system and can potentially compromise the integrity of sensitive data. Combining biometrics with cryptography is seen as a possible solution but any biometric cryptosystem must be able to overcome small variations present between different acquisitions of the same biometric in order to produce consistent keys. This paper discusses a new method which uses an entropy based feature extraction process coupled with Reed-Solomon error correcting codes that can generate deterministic bit-sequences from the output of an iterative one-way transform. The technique is evaluated using 3D face data and is shown to reliably produce keys of suitable length for 128-bit Advanced Encryption Standard (AES).
A new method for segmenting white blood cells nuclei in microscopic images is presented. Challenges to accurate segmentation include intra-class variation of the nuclei cell boundaries, non-uniform illumination, and changes in the cell topology due to its orientation and stage of maturity. In this research, level set methods and geometric active contours are used to segment the nucleus of white blood cells from the cytoplasm and the cell wall. Level set methods use morphological operations to estimate an initial cell boundary and are fully automated. Geometric active contours are less computationally complex and adapt better to the curve topology of the cell boundary than parametric active contours, which have been previously used for white blood cell segmentation. Segmentation performance is compared with other segmentation methods using the Berkeley benchmark database and the proposed method is shown to be superior using various indices.
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