Afterwards, the comparative evaluation and the corresponding resu

Afterwards, the comparative evaluation and the corresponding results are presented selleck inhibitor in Section 5. Finally, conclusions and future work are introduced in Section 6.2.?Literature ReviewHand biometric systems have evolved from early approaches which considered flat-surface and pegs to guide the placement of the user��s hand [1�C3], to completely platform-free, non-contact techniques were user collaboration is almost not required [4�C7]. This development can be classified into three categories according to the image acquisition criteria [8]:Constrained and contact based. Systems requiring a flat platform and pegs or pins to restrict hand degree of freedom [2,3].Unconstrained and contact based. Peg-free scenarios, although still requiring a platform to place the hand, like a scanner [6,9].
Unconstrained and contact-free. Platform-free and contact-less scenarios where neither pegs nor platform are required for hand image acquisition [5,10].In fact, at present, contact-less hand biometrics approaches are increasingly being considered because of their properties in user acceptability, hand distortion avoidance and hygienic concerns [11,12], and their promising capability to be extended and applied to daily devices with less requirements in terms of image quality acquisition or speed processor [9,10,13].In addition, hand biometrics gather a wide variety of distinctive aspects and parameters to identify individuals, considering whether fingers [7,14,15], hand geometric features [2,3,6,15,16], hand contour [2,10,17], hand texture and palmprint [8,18] or some fusion of these former characteristics [7,14,19].
More specifically, geometrical features have received notorious attention and research efforts, in comparison to other hand parameters. Methods based on this strategy (like widths, angles and lengths) reduce the information given in a hand sample to a N-dimensional vector, proposing any metric distance for computing the similarity between two samples [20].In opposition to this method, several schemes are proposed in literature applying different probabilistic and machine learning techniques to classify properly user hand samples. The most common techniques are k-Nearest Neighbours [21], Gaussian Mixture Models [3,22], na?ve AV-951 Bayes [21] or Support Vector Machines [9,18,21], which is certainly the most extended technique in hand biometrics due to their performance in template classification.
Nonetheless, these latter strategies present several drawbacks in comparison with distance-based approaches in terms of computational cost and efficiency, since probabilistic-based strategies require other user samples to conform an individual template. In other words, systems based on a classifier approach are trained for each of exactly the enrolled persons, requiring samples from other enrolled individuals for a separate classification. This fact may become a computational challenge, for large-population systems [20].

Leave a Reply

Your email address will not be published. Required fields are marked *

*

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>