LEARNING-FREE DEEP FEATURES FOR MULTISPECTRAL PALM-PRINT CLASSIFICATION

The feature extraction step is a major and crucial step in analyzing and understanding raw data as it has a considerable impact on the system accuracy. Unfortunately, despite the very acceptable results obtained by many handcrafted methods, they can have difficulty representing the features in the case of large databases or with strongly correlated samples. In this context, we proposed a new, simple and lightweight method for deep feature extraction. Our method can be configured to produce four different deep features, each controlled to tune the system accuracy. We have evaluated the performance of our method using a multispectral palmprint based biometric system and the experimental results, using the CASIA database, have shown that our method has high accuracy compared to many current handcrafted feature extraction methods and many well known deep learning based methods

ENHANCING MEDICAL DATA SECURITY IN E-HEALTH SYSTEMS USING BIOMETRIC-BASED WATERMARKING

In the field of Electronic Health (e-Health), Electronic Health Records (EHR) are transmitted between health professionals using e-Health systems for cooperative medical practice, medical monitoring, telemedical expertise, and telemedical imaging. Medical images are a crucial component of EHR and are used in various aspects of telemedicine systems such as expertise, consultation, teaching, and research. However, protecting the authenticity and copyrights of medical images is essential to prevent duplication, modification, or unauthorized distribution. This paper proposes a robust medical image copyright protection method that uses patient palm-print template as watermark and Lorenz chaotic map for template concealing and selecting the appropriate embedding positions in medical images. The novelty of the method lies in optimizing the expected number of modifications per pixel of the medical images after being watermarked. Experimental results indicate that this approach has a high performance with a genuine accept rate of 99.86% and can withstand various image processing attacks, including Gaussian noise, compression, and image rotations, while ensuring personal data security during telemedicine data exchange

An efficient multi-spectral palmprint identification using contourlet decomposition and Hidden Markov Model

Automatic personal identification is playing an important role in security systems. Biometrics technologies has been emerging as a new and effective methods to achieve accurate and reliable identification results. A number of biometric traits exist and are in use in various applications. Palmprint is one of the relatively new biometrics due to its stable and unique characteristics. In this paper, multi-spectral information for the unique palmprint are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. For that, the palm lines are characterized by the contourlet coefficients sub-bands and compressed using the Principal Components Analysis (PCA). Subsequently, we use the Hidden Markov Model (HMM) for modeling. Finally, log-likelihood scores are used for palmprint matching. Experimental results show that our proposed scheme yields the best performance for identifying palmprints and it is able to provide an excellent identification rate and provide more security

Robust multispectral palmprint identification system by jointly using Contourlet decomposition & Gabor filter response

In current society, reliable identification and verification of individuals are becoming more and more necessary tasks for many fields, not only in police environment, but also in civilian applications, such as access control or financial transactions. Biometric systems are used nowadays in these fields, offering greater convenience and several advantages over traditional security methods based on something that you know (password) or something that you have (keys). In this paper, we propose an efficient online personal identification system based on Multi-Spectral Palmprint (MSP) images using Contourlet Transform (CT) and Gabor Filter (GF) response. In this study, the spectrum image is characterized by the contourlet coefficients sub-bands. Then, we use the Hidden Markov Model (HMM) for modeling the observation vector. In addition, the same spectrum is filtered by the Gabor filter. The real and imaginary responses of the filtering image are used to create another observation vector. Subsequently, the two sub-systems are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. Our experimental results show the effectiveness and reliability of the proposed method, which brings both high identification and accuracy rate

Are infrared images reliable for palmprint based personal identification systems?

Several studies for palmprint-based person identification have focused on the use of palmprint images captured in the visible part of the spectrum. However, to a possible improvement of the existing palmprint systems, the proposed work concerned with the use of infrared palmprint images for the palmprint identification system. For that, a comparison of infrared palmprint images versus gray level and color image is given. At the features-extraction stage the features are generated by the method of Principal Component Analysis (PCA). This feature-extraction technique has been widely used for pattern recognition, as well as in the field of biometrics. The proposed scheme is tested and evaluated using PolyU multispectral palmprint database of 400 users. Our experimental results show that the infrared spectrum achieves the best result. Also, color image present three spectrums, for that, we propose a score level and image level fusion schemes to integrate these colors information

A robust palmprint identification system using Histogram of Oriented Gradients and multi-classifiers

Nowadays, identification of persons has a great importance for information protection and access control. Thus, automatic person identification based on biometrics has become a focus of interest both for research and commercial purposes. Among the biometrics used, palmprint identification is one of the most stable and reliable technology. Some desirable properties such as uniqueness, stability, and non invasiveness make this technology suitable for highly reliable person identification. In this paper, a method is proposed based on Histogram of Oriented Gradients (HOG) descriptors for palmprint identification. This method utilized the fusion, at matching score level, of some classifiers (Radial Basis Function (RBF), Random Forest Transform (RTF) and Support Vector Machine (SVM)) to improve the performance in identification accuracy. Extensive experiments show the effectiveness of the proposed method with respect to the identification rate

Learning-free deep features for multispectral palm-print classification

The feature-extraction step is a major and crucial step in analyzing and understanding raw data, as it has a considerable impact on system accuracy. Despite the very acceptable results that have been obtained by many handcrafted methods, these can unfortunately have difficulty representing features in the cases of large databases or with strongly correlated samples. In this context, we attempt to examine the discriminability of texture features by proposing a novel, simple, and lightweight method for deep feature extraction to characterize the discriminative power of different textures. We evaluated the performance of our method by using a palm print-based biometric system, and the experimental results (using the CASIA multispectral palm--print database) demonstrate the superiority of the proposed method over the latest handcrafted and deep methods

Efficient person identification by fusion of multiple palmprint representations

The automatic person identification is a significant component in any security biometric system because of the challenges and the significant number of the applications that require a high safety. A biometric system based solely on one template (representation) is often not able to meet such desired performance requirements. Identification based on multiple representations represents a promising tendency. In this context, we propose here a multi-representation biometric system for person recognition using palm images and by integrating two different representations of the palmprint. Two ensembles of matchers that use two different feature representation schemes of the images are considered. The two different feature extraction methods are the block based 2D Discrete Cosine Transform (2D-DCT) and the phase information in 2D Discrete Fourier Transform (2D-DFT) that are complementing each other in terms of identification accuracy. Finally the two ensembles are combined and the fusion is applied at the matching-score level. Using the PolyU palmprint database, The results showed the effectiveness of the proposed multi-representation biometric system in terms of the recognition rate