Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

Biometric face recognition using multilinear projection and artificial intelligence

PhD ThesisNumerous problems of automatic facial recognition in the linear and multilinear subspace learning have been addressed; nevertheless, many difficulties remain. This work focuses on two key problems for automatic facial recognition and feature extraction: object representation and high dimensionality. To address these problems, a bidirectional two-dimensional neighborhood preserving projection (B2DNPP) approach for human facial recognition has been developed. Compared with 2DNPP, the proposed method operates on 2-D facial images and performs reductions on the directions of both rows and columns of images. Furthermore, it has the ability to reveal variations between these directions. To further improve the performance of the B2DNPP method, a new B2DNPP based on the curvelet decomposition of human facial images is introduced. The curvelet multi- resolution tool enhances the edges representation and other singularities along curves, and thus improves directional features. In this method, an extreme learning machine (ELM) classifier is used which significantly improves classification rate. The proposed C-B2DNPP method decreases error rate from 5.9% to 3.5%, from 3.7% to 2.0% and from 19.7% to 14.2% using ORL, AR, and FERET databases compared with 2DNPP. Therefore, it achieves decreases in error rate more than 40%, 45%, and 27% respectively with the ORL, AR, and FERET databases. Facial images have particular natural structures in the form of two-, three-, or even higher-order tensors. Therefore, a novel method of supervised and unsupervised multilinear neighborhood preserving projection (MNPP) is proposed for face recognition. This allows the natural representation of multidimensional images 2-D, 3-D or higher-order tensors and extracts useful information directly from tensotial data rather than from matrices or vectors. As opposed to a B2DNPP which derives only two subspaces, in the MNPP method multiple interrelated subspaces are obtained over different tensor directions, so that the subspaces are learned iteratively by unfolding the tensor along the different directions. The performance of the MNPP has performed in terms of the two modes of facial recognition biometrics systems of identification and verification. The proposed supervised MNPP method achieved decrease over 50.8%, 75.6%, and 44.6% in error rate using ORL, AR, and FERET databases respectively, compared with 2DNPP. Therefore, the results demonstrate that the MNPP approach obtains the best overall performance in various learning scenarios

A new approach to face recognition using Curvelet Transform

Multiresolution tools have been profusely employed in face recognition. Wavelet Transform is the best known among these multiresolution tools and is widely used for identification of human faces. Of late, following the success of wavelets a number of new multiresolution tools have been developed. Curvelet Transform is a recent addition to that list. It has better directional ability and effective curved edge representation capability. These two properties make curvelet transform a powerful weapon for extracting edge information from facial images. Our work aims at exploring the possibilities of curvelet transform for feature extraction from human faces in order to introduce a new alternative approach towards face recognition

Biometric Applications Based on Multiresolution Analysis Tools

This dissertation is dedicated to the development of new algorithms for biometric applications based on multiresolution analysis tools. Biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individual\u27s identity. Biometrics can measure physiological, behavioral, physical and chemical characteristics of an individual. Physiological characteristics are based on measurements derived from direct measurement of a part of human body, such as, face, fingerprint, iris, retina etc. We focussed our investigations to fingerprint and face recognition since these two biometric modalities are used in conjunction to obtain reliable identification by various border security and law enforcement agencies. We developed an efficient and robust human face recognition algorithm for potential law enforcement applications. A generic fingerprint compression algorithm based on state of the art multiresolution analysis tool to speed up data archiving and recognition was also proposed. Finally, we put forth a new fingerprint matching algorithm by generating an efficient set of fingerprint features to minimize false matches and improve identification accuracy. Face recognition algorithms were proposed based on curvelet transform using kernel based principal component analysis and bidirectional two-dimensional principal component analysis and numerous experiments were performed using popular human face databases. Significant improvements in recognition accuracy were achieved and the proposed methods drastically outperformed conventional face recognition systems that employed linear one-dimensional principal component analysis. Compression schemes based on wave atoms decomposition were proposed and major improvements in peak signal to noise ratio were obtained in comparison to Federal Bureau of Investigation\u27s wavelet scalar quantization scheme. Improved performance was more pronounced and distinct at higher compression ratios. Finally, a fingerprint matching algorithm based on wave atoms decomposition, bidirectional two dimensional principal component analysis and extreme learning machine was proposed and noteworthy improvements in accuracy were realized

Cascade of classifier ensembles for reliable medical image classification

Medical image analysis and recognition is one of the most important tools in modern medicine. Different types of imaging technologies such as X-ray, ultrasonography, biopsy, computed tomography and optical coherence tomography have been widely used in clinical diagnosis for various kinds of diseases. However, in clinical applications, it is usually time consuming to examine an image manually. Moreover, there is always a subjective element related to the pathological examination of an image. This produces the potential risk of a doctor to make a wrong decision. Therefore, an automated technique will provide valuable assistance for physicians. By utilizing techniques from machine learning and image analysis, this thesis aims to construct reliable diagnostic models for medical image data so as to reduce the problems faced by medical experts in image examination. Through supervised learning of the image data, the diagnostic model can be constructed automatically. The process of image examination by human experts is very difficult to simulate, as the knowledge of medical experts is often fuzzy and not easy to be quantified. Therefore, the problem of automatic diagnosis based on images is usually converted to the problem of image classification. For the image classification tasks, using a single classifier is often hard to capture all aspects of image data distributions. Therefore, in this thesis, a classifier ensemble based on random subspace method is proposed to classify microscopic images. The multi-layer perceptrons are used as the base classifiers in the ensemble. Three types of feature extraction methods are selected for microscopic image description. The proposed method was evaluated on two microscopic image sets and showed promising results compared with the state-of-art results. In order to address the classification reliability in biomedical image classification problems, a novel cascade classification system is designed. Two random subspace based classifier ensembles are serially connected in the proposed system. In the first stage of the cascade system, an ensemble of support vector machines are used as the base classifiers. The second stage consists of a neural network classifier ensemble. Using the reject option, the images whose classification results cannot achieve the predefined rejection threshold at the current stage will be passed to the next stage for further consideration. The proposed cascade system was evaluated on a breast cancer biopsy image set and two UCI machine learning datasets, the experimental results showed that the proposed method can achieve high classification reliability and accuracy with small rejection rate. Many computer aided diagnosis systems face the problem of imbalance data. The datasets used for diagnosis are often imbalanced as the number of normal cases is usually larger than the number of the disease cases. Classifiers that generalize over the data are not the most appropriate choice in such an imbalanced situation. To tackle this problem, a novel one-class classifier ensemble is proposed. The Kernel Principle Components are selected as the base classifiers in the ensemble; the base classifiers are trained by different types of image features respectively and then combined using a product combining rule. The proposed one-class classifier ensemble is also embedded into the cascade scheme to improve classification reliability and accuracy. The proposed method was evaluated on two medical image sets. Favorable results were obtained comparing with the state-of-art results

Unifying the Visible and Passive Infrared Bands: Homogeneous and Heterogeneous Multi-Spectral Face Recognition

Face biometrics leverages tools and technology in order to automate the identification of individuals. In most cases, biometric face recognition (FR) can be used for forensic purposes, but there remains the issue related to the integration of technology into the legal system of the court. The biggest challenge with the acceptance of the face as a modality used in court is the reliability of such systems under varying pose, illumination and expression, which has been an active and widely explored area of research over the last few decades (e.g. same-spectrum or homogeneous matching). The heterogeneous FR problem, which deals with matching face images from different sensors, should be examined for the benefit of military and law enforcement applications as well. In this work we are concerned primarily with visible band images (380-750 nm) and the infrared (IR) spectrum, which has become an area of growing interest.;For homogeneous FR systems, we formulate and develop an efficient, semi-automated, direct matching-based FR framework, that is designed to operate efficiently when face data is captured using either visible or passive IR sensors. Thus, it can be applied in both daytime and nighttime environments. First, input face images are geometrically normalized using our pre-processing pipeline prior to feature-extraction. Then, face-based features including wrinkles, veins, as well as edges of facial characteristics, are detected and extracted for each operational band (visible, MWIR, and LWIR). Finally, global and local face-based matching is applied, before fusion is performed at the score level. Although this proposed matcher performs well when same-spectrum FR is performed, regardless of spectrum, a challenge exists when cross-spectral FR matching is performed. The second framework is for the heterogeneous FR problem, and deals with the issue of bridging the gap across the visible and passive infrared (MWIR and LWIR) spectrums. Specifically, we investigate the benefits and limitations of using synthesized visible face images from thermal and vice versa, in cross-spectral face recognition systems when utilizing canonical correlation analysis (CCA) and locally linear embedding (LLE), a manifold learning technique for dimensionality reduction. Finally, by conducting an extensive experimental study we establish that the combination of the proposed synthesis and demographic filtering scheme increases system performance in terms of rank-1 identification rate

Sparse and Redundant Representations for Inverse Problems and Recognition

Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented

Use of Coherent Point Drift in computer vision applications

This thesis presents the novel use of Coherent Point Drift in improving the robustness of a number of computer vision applications. CPD approach includes two methods for registering two images - rigid and non-rigid point set approaches which are based on the transformation model used. The key characteristic of a rigid transformation is that the distance between points is preserved, which means it can be used in the presence of translation, rotation, and scaling. Non-rigid transformations - or affine transforms - provide the opportunity of registering under non-uniform scaling and skew. The idea is to move one point set coherently to align with the second point set. The CPD method finds both the non-rigid transformation and the correspondence distance between two point sets at the same time without having to use a-priori declaration of the transformation model used. The first part of this thesis is focused on speaker identification in video conferencing. A real-time, audio-coupled video based approach is presented, which focuses more on the video analysis side, rather than the audio analysis that is known to be prone to errors. CPD is effectively utilised for lip movement detection and a temporal face detection approach is used to minimise false positives if face detection algorithm fails to perform. The second part of the thesis is focused on multi-exposure and multi-focus image fusion with compensation for camera shake. Scale Invariant Feature Transforms (SIFT) are first used to detect keypoints in images being fused. Subsequently this point set is reduced to remove outliers, using RANSAC (RANdom Sample Consensus) and finally the point sets are registered using CPD with non-rigid transformations. The registered images are then fused with a Contourlet based image fusion algorithm that makes use of a novel alpha blending and filtering technique to minimise artefacts. The thesis evaluates the performance of the algorithm in comparison to a number of state-of-the-art approaches, including the key commercial products available in the market at present, showing significantly improved subjective quality in the fused images. The final part of the thesis presents a novel approach to Vehicle Make & Model Recognition in CCTV video footage. CPD is used to effectively remove skew of vehicles detected as CCTV cameras are not specifically configured for the VMMR task and may capture vehicles at different approaching angles. A LESH (Local Energy Shape Histogram) feature based approach is used for vehicle make and model recognition with the novelty that temporal processing is used to improve reliability. A number of further algorithms are used to maximise the reliability of the final outcome. Experimental results are provided to prove that the proposed system demonstrates an accuracy in excess of 95% when tested on real CCTV footage with no prior camera calibration