Salim Chitroub

Fusion of Finger-Knuckle-Print and Palmprint for an Efficient Multi-Biometric System of Person Recognition

Biometric system has been actively emerging in various industries for the past few years, and it is continuing to roll to provide higher security features for access control system. Many types of unimodal biometric systems have been developed. However, these systems are only capable to provide low to middle range of security feature. Thus, for higher security feature, the combination of two or more unimodal biometrics (multiple modalities) is required. In this paper, we propose a multimodal biometric system for person recognition using hand images and by integrating two different modalities palmprint and Finger-Knuckle-Print (FKP). Addressing this problem we propose an efficient matching algorithm based on Phase-Correlation Function (PCF) and using the two biometric modalities the palmprint and the FKP. The two modalities are combined and the fusion is applied at the matching-score level. The experimental results showed that the designed system achieves an excellent recognition rate and provide more security than unimodal biometric-based system.

2D and 3D palmprint information, PCA and HMM for an improved person recognition performance

Biometric systems based on a single source of information suffer from limitations such as the lack of uniqueness, non-universality of the chosen biometric trait, noisy data and spoof attacks. Multimodal biometrics are relatively new systems that overcome those problems. These systems fuse information from multiple sources in order to achieve the better person recognition performance. In this paper, the 2D and 3D information of palmprint are integrated in order to construct an efficient multimodal biometric system based on fusion at matching score level and at feature extraction level. The observation vectors are created independently either from the original data of the two modalities 2D and 3D palmprint or from their rotation invariant variance measures applied on textures. On each modality or its corresponding invariant texture, we have applied the Principal Component Analysis PCA for reducing dimension of the feature vector. We have also used the multi-scale wavelet decomposition for each modality and the results of decomposition are combined and compressed using PCA for selecting the feature vectors. Subsequently, we have used the Hidden Markov Model HMM for modeling the feature vectors. Finally, Log-likelihood scores are used for palmprint evaluation. We note that the selected principal components of two modalities are fused at feature level and at matching score level. The proposed scheme is tested and evaluated using PolyU 2D and 3D palmprint database of 250 persons. Our experimental results show the effectiveness and reliability of the proposed system, which brings high identification accuracy rate.

Principal component analysis of multispectral images using neural network

The conventional approach of PCA applied to multispectral images involves the computation of the spectral image covariance matrix and application of diagonalization procedures for extracting the eigenvalues and corresponding eigenvectors. When the number of spectral images grows significantly, the matrix computation and manipulation become practically inefficient and inaccurate due to round-off errors. These deficiencies make the conventional scheme inefficient for this application. We propose a neural network model that performs the PCA directly from the original spectral images without any additional non-neuronal computations or preliminary matrix estimation. The design of the network topology and input/output representation as well as the design of learning algorithms are carefully established. The convergence of the model is studied. Its application has been realized on real multispectral images. The obtained results show that the model performs well.

Classifier combination and score level fusion: concepts and practical aspects

This review article is about the classifier combination and score level fusion, which have received considerable attention in the research field over the past decade. The classifier combination covers research concerning the algorithms and methodologies that describe classifier combinations from a pattern recognition perspective. For the score fusion technique, this article addresses the fusion problem in those situations where the feature representations of the data are not available. These two concepts are discussed from the point of view of their mathematical formulations. Some practical aspects and applications are also treated.

Multimodal biometric person recognition system based on fingerprint & Finger-Knuckle-Print using correlation filter classifier

Biometrics is an effective technology for personnel identity recognition, but uni-modal biometric systems which use a single trait for recognition will suffer from problems like noisy sensor data, non-universality, lack of distinctiveness of the biometric trait, and spoof attacks. These problems can be tackled by using multi-biometrics in the system. Hand-based person recognition provides a reliable, low-cost and user-friendly viable solution for a range of access control applications. As one of the most popular biometric traits, fingerprints (FP) are widely used in personal recognition. However, a novel hand-based biometric feature, Finger-Knuckle-Print (FKP), has attracted an increasing amount of attention. In this paper, FP and FKP are integrated in order to construct an efficient multi-biometric recognition system based on matching score level and image level fusion. In this study we use the minimum average correlation energy (MACE) and Unconstrained MACE (UMACE) filters in conjunction with two correlation plane performance measures, max peak value and peak-to-sidelobe ratio, to determine the effectiveness of this method. The experimental results showed that the designed system achieves an excellent recognition rate on the Hong Kong polytechnic university (PolyU) FKP and high resolution fingerprint database.

Gaussian modeling and Discrete Cosine Transform for efficient and automatic palmprint identification

Automatic personal identification using biometric information is playing a more and more important role in applications such as public security, access control, banking, etc. Palmprint identification is a subcategory of biometrics identification, which can efficiently used to identify the people. It is for this reason that palmprint-based identification is becoming increasingly popularity in recent years. In this paper, we present a novel scheme for palmprint identification using the multi-variate Gaussian Probability Density Function (GPDF) and two-dimensional Block based Discrete Cosine Transform (2D-BDCT). In this method, a palmprint is firstly divided into overlapping and equal-sized blocks, and then, applies the discrete cosine transform over each block. By using zigzag scan order (starting at the top-left) each transform block is reordered to produce the observation vector. Subsequently, we use the Gaussian probability density function for modeling the feature vector of each palmprint. Finally, Log-likelihood scores are used for palmprint matching. The proposed scheme is validated for their efficacy on PolyU palmprint database of 100 users. Our experimental results show the effectiveness and reliability of the proposed approach, which brings both high identification accuracy rate.

Compound PCA-ICA neural network model for enhancement and feature extraction of multi-frequency polarimetric SAR imagery

Through its demixing operation, the potential use of independent components analysis (ICA) for multi-frequency polarimetric SAR imagery enhancement and feature extraction is demonstrated. A compound PCA-ICA neural network model is proposed, which consists of two levels of processing. The first one is the simultaneous diagonalization of the signal and signal-dependent noise covariance matrices using PCA transforms. The goal is to provide the PC images that are decorrelated and in which the SNR is improved. The second one consists of separating the noise from these images by providing new IC images in which the speckle is reduced. These images approach the PC ones and may be different only in their order and contrast. As a quantitative criterion, the contrast ratio is used, which value is smaller when the speckle is reduced. The model has been applied to the SIR-C data. The extracted features are quite effective for scene interpretation.

Person’s Recognition Using Palmprint Based on 2D Gabor Filter Response

Palmprint recognition is very important in automatic personal identification. The objective of this study is to develop an efficient prototype system for an automatic personal identification using palmprint technology. In this work, a new texture feature based on Gabor filter is proposed. First, the region of interest was filtering by 2D Gabor filter, then, the principal lines, wrinkles, and ridges, are extracted using a simple thresholding of the complex magnitude of the filtred ROI, Latterly, the candidate was found by matching process. We have tested our algorithm scheme over several images taken from a palmprint database collected by hong kong polytechnic university. The testing results showed that the designed system achieves an acceptable level of performance.

PCA-ICA neural network model for POLSAR images analysis

The POLSAR images are modeled by a mixture model that results from the product of two independent models, one characterizes the target response and the other characterizes the speckle phenomenon. For scene interpretation, it is desirable to separate between the target response and the speckle. For this purpose, a PCA-ICA neural network model is proposed. Based on its rigorous statistical formulation, a neuronal approach for the simultaneous diagonalisation of the signal and noise covariance matrices using a PCA transform is proposed. The PC images are uncorrelated and having an improved SNR. However, the speckle is a non-Gaussian multiplicative noise, the higher order statistics contain additional information about it. The ICA method is used to separate the speckle from the PC images and provide new IC images that have an improved contrast. The method has been applied to real POLSAR images. The extracted features are quite effective for the scene interpretation.

Do multispectral palmprint images be reliable for person identification

This paper is concerned with an investigation of multispectral palmprint images for improving person identification by replying to the question: can multispectral palmprint images be reliable for such purpose? Two biometric systems are then proposed. In the first system, each spectral image is aligned and then used for feature extraction using 1D Log-Gabor filter. The features are encoded and Hamming distance is used for matching. The fusion at matching score level is used before the decision making. The second system is based on multiresolution analysis for feature extraction. The spectral images are decomposed into frequency sub-images with different levels of decomposition. The extracted coefficients are used as features. The MGPDF is used for modeling the features and Log-Likelihood scores are used for matching. Fusion at the matching score level is used before decision making. A comparative study between the two systems is then developed. The experimental results are demonstrated using the PolyU multispectral database and the results show that the two proposed systems are more effective when using multispectral images than their monospectral counterpart images.