Abdallah Meraoumia

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.

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.

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.

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.

Fusion of multispectral palmprint images for automatic person identification

Reliability and accuracy in personal identification system is a dominant concern to the security world. Biometric has gained much attention in this subject recently. Many types of personal identification systems have been developed, and palmprint identification is one of the emerging technologies. This paper presents a novel biometric technique to automatic personal identification system using multispectral palmprint technology. In this method, each of spectrum images are aligned and then used to extract palmprint features using 1D log-Gabor filter. These features are then examined for their individual and combined performances. Finally, the hamming distance is used for matching of palmprint features. The experimental results showed that the proposed method achieve an excellent identification rate and provide more security.

An automated ear identification system using Gabor filter responses

About some years ago, several biometric technologies are considered mature enough to be a new tool for security and ear-based person identification is one of these technologies. This technology provides a reliable, low cost and user-friendly viable solution for a range of access control applications. In this paper, we propose an efficient online personal identification system based on ear images. In this purpose, the identification algorithm aims to extract, for each ear, a specific set of features. Based on Gabor filter response, three ear features have been used in order to extract different and complementary information: phase, module and a combination of the real and imaginary parts. Using these features, several combinations are tested in the fusion phase in order to achieve an optimal multi-representation system which leads to a better identification accuracy. The obtained experimental results show that the system yields the best performance for identifying a person and it is able to provide the highest degree of biometrics-based system security.

An Efficient Palmprint Identification System Using Multispectral and Hyperspectral Imaging

Ensuring the security of individuals is becoming an increasingly important problem in a variety of applications. Biometrics technology that relies on the physical and/or behavior human characteristics is capable of providing the necessary security over the standard forms of identification. Palmprint recognition is a relatively new one. Almost all the current palmprint-recognition systems are mainly based on image captured under visible light. However, multispectral and hyperspectral imaging have been recently used to improve the performance of palmprint identification. In this paper, the MultiSpectral Palmprint (MSP) and HyperSpectral Palmprint (HSP) are integrated in order to construct an efficient multimodal biometric system. The observation vector is based on Principal Components Analysis (PCA). Subsequently, HiddenMarkov Model (HMM) is used for modeling this vector. The proposed scheme is tested and evaluated using 350 users. Our experimental results show the effectiveness and reliability of the proposed system, which brings high identification accuracy rate.

2D and 3D palmprint information and Hidden Markov Model for improved identification 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. Multibiometrics are relatively new systems that overcome those problems. These systems fuse information from multiple biometric sources in order to achieve better identification performance. In this paper, 2D and 3D palmprint are integrated in order to construct an efficient multibiometric identification system based on matching score level fusion. For that, the texture information is characterized by the rotation invariant VARiance measures (VAR) and compressed using the Principal Components Analysis (PCA). Subsequently, we use the Hidden Markov Model (HMM) for modeling the feature vector of each palmprint. Finally, Log-likelihood scores are used for palmprint evaluation. The proposed scheme is tested and evaluated using PolyU 2D-3D palmprint database of 250 users. Our experimental results show the effectiveness and reliability of the proposed system, which brings high identification accuracy rate.