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The need for automated secure access to physical or virtual environments, especially for personalized services, is growing. These needs require reliable means to verify the identity of a person who is having access to the system. However, conventional means, such as passwords or magnetic cards associated with a personal code, have a number of shortcomings. For instance, a password may be forgotten or stolen by another individual, or even given to someone else and access cards may also be lost or stolen.
This is how the science of automatic identification of people through the exploitation of their unique physical and behavioral traits, such as face, signature, voice, fingerprints, shape of the hand, palmprint, etc., appeared the most reliable solution. Each of these different traits is called biometric modality.
Among these physical modalities, we are interested in the palmprint, which is the most mature biometric technology that has been used for more than a century. Hitherto, more attention has been paid to palmprint identification systems through research teams because it presents diverse advantages compared to other modalities in the biometric system such as its usability, stability over time, low cost, affordable materials, high recognition accuracy, etc.
The palmprint is particularly solicited in biometrics: it is the inside of the hand between the wrist and the roots of the fingers. It illustrates the most discriminating physical characteristic for the recognition of its skin patterns including both the principal lines and the texture representations, which are appropriated for the use of the real application within palmprint recognition.
In fact, an overview of the related work methods about palmprint identification applications is presented. Till now, these already existing applications can be divided into two popular categories, Global approach and Structural approach, based mainly on the extraction and the analysis of the different palmprint representations.
Structural approaches: They analyze the structure of the palmprint and include the palmprint lines, such as the principal lines (cf., (Han, Cheng, Lin, & Fan, 2003), (Mokni, Drira, & Kherallah, 2017a), etc.), the wrinkles (cf., (Chen, Zhang, & Rong, 2001), etc.), the ridges and minutiae (cf., (Duta, Jain, & Mardia, 2002), etc.) for represented the palmprint pattern.
Although this type of approach relies on taking into account the structural particularity of the palmprint representations since it is stable over time, unluckily, those representations alone cannot provide satisfactory information to identify the person efficiently.
Global approaches: They use the whole area of the palmprint as input to their recognition algorithm. Over recent years, researchers have been interested in this type of approach that presents different descriptors or methods to analyze the texture pattern of an image. In fact, different proposals have been presented. We distinguish three methodologies based on the texture feature analysis such as Statistical, Frequency and Model. Each one of them involves various descriptors.
In fact, (1) Statistical methodology includes Eigenpalms (cf.,(Lu, Zhang, & Wang, 2003), etc.), Fisherpalms (cf., (Wu, Zhang, & Wang, 2003), etc.), Local Binary Pattern (LBP) (cf., (Hammami, Ben-Jemaa, & Ben-Abdallah, 2014), etc.), Gray Level Co-occurrence Matrix (GLCM) (cf., (Latha & Prasad, 2015)), (Mokni, Elleuch, & Kherallah, 2016), etc.), (2) Frequency methodology involves different descriptors such as Gabor filters (GF) (cf., (Kumar & Shekhar, 2010), (Ben-Khalifa, Rzouga, & BenAmara, 2013), etc.), Scale-Invariant Feature Transform (SIFT) (cf., (Hammami et al., 2014)), Wavelets (cf., (Kekre, Sarode, & Tirodkar, 2012), etc.), and (3) Model methodology groups Blanket or Fractal, Multi-Fractal dimensions (cf., (Guo, Zhou, & Wang, 2014), etc.).