Investigation of Probabilistic Graphical Model Algorithms for Palm print Verification

Abstract

Palmprint is emerging as a popular biometric based personal identification technique and has been found to be more advantageous than fingerprint because of its larger area to capture more distinctive features. Most of the fingerprint discriminative features are also found in Palmprints. Palmprint feature extraction is one of the most important stages in the verification process. The robustness of the system depends on the feature extraction methodology and its ability to extract features from the palmprint. In this paper we propose a global feature extraction based on the Discrete Cosine Transform and investigate the efficiency of BayesNet algorithm for verification. This work also investigated the effect of feature reduction using information gain on the proposed methodology. This work utilized 50 palmprints of different users from the palmprint database provided by the Hong Kong Polytechnic University (HK-PolyU) to evaluate the proposed methodology.

Advancements in Synthetic Generation for Contactless Palmprint Biometrics Using StyleGan2-ADA and StyleGAN3

“…Palmprint refers to an impression of the palm on a surface. A palmprint contains rich intrinsic features, including the principal lines and wrinkles (figure 1) [1] [5] [6] and abundant ridge and minutiae-based features similar to a fingerprint [3].These significant features make a palmprint very useful in the field of biometrics because these palmprint features have the potential to achieve high accuracy and reliable performance for personal verification and identification [4,8]. Many techniques have been proposed for palmprint recognition using minutiae-based features, geometrybased features, transformed-based features, [7] and more.…”

“…Recently, most methods in the literature consider Deep Learning due to its high recognition accuracy and the capability to adapt to biometric. samples captured in heterogeneous and less-constrained conditions [8]. Current state-of-the-art palmprint recognition systems rely on large datasets.…”

“…One of the main reasons to generate synthetic images is low cost, high efficiency, and testing privacy. Moreover, the quality and resolution of images generated by generative adversarial networks (GANs) have experienced significant advancements recently [7][8][9]. The architecture of a standard GAN generator operates in a similar fashion: initially, it creates rough, low-resolution attributes that are progressively refined 2 through upsampling layers.…”

Multi-Grained Deep Cascade Learning for ECG Biometric Recognition

“…At present, many kinds of biological characteristics can be used for personal identification and they can be mainly divided into two categories: physiological characteristics and behavioural characteristics. Common physiological characteristics include human faces [2], fingerprints [3], hands [4,5], irises [6,7], and ears [8,9]; behavioural characteristics include voices [10], gaits [11], signatures [12,13], and keystrokes [14,15]. However, these human biological characteristics are easy to falsify, which makes this type of approach to personal identification vulnerable.…”