Efficient palmprint biometric identification systems using deep learning and feature selection methods

Abstract

Over the past two decades, several studies have paid great attention to biometric palmprint recognition. Recently, most methods in literature adopted deep learning due to their high recognition accuracy and the capability to adapt with different acquisition palmprint images. However, high-dimensional data with a large number of uncorrelated and redundant features remain a challenge due to computational complexity issues. Feature selection is a process of selecting a subset of relevant features, which aims to decrease the dimensionality, reduce the running time, and improve the accuracy. In this paper, we propose efficient unimodal and multimodal biometric systems based on deep learning and feature selection. Our approach called simplified PalmNet–Gabor concentrates on the improvement of the PalmNet for fast recognition of multispectral and contactless palmprint images. Therefore, we used Log-Gabor filters in the preprocessing to increase the contrast of palmprint features. Then, we reduced the number of features using feature selection and dimensionality reduction procedures. For the multimodal system, we fused modalities at the matching score level to improve system performance. The proposed method effectively improves the accuracy of the PalmNet and reduces the number of features as well the computational time. We validated the proposed method on four public palmprint databases, two multispectral databases, CASIA and PolyU, and two contactless databases, Tongji and PolyU 2D/3D. Experiments show that our approach achieves a high recognition rate while using a substantially lower number of features.

A Novel Feature Selection Method Based on Bio-inspired Artificial Intelligence for The Feature Selection of Bio-metric Data

“…In the first technique, the palm vein images are split into 5 overlapping sub-area and described using the Binarized Statistical Image Features descriptor method (BSIF), and in the second method, each palm vein image is descriptorized using a model that employs convolutional neural networks. Trabelsi et al [10] designed a uni-modal and multi-modal bio-metric systems that use feature selection and deep learning methods. The main objective of the proposed methodology termed PalmNet-Gabor is to accelerate the recognition of multi-spectral and contactless palmprint images.…”

“…The latter considers both the number of features that have been chosen and the accuracy of the system simultaneously. The objective function is expressed by the formula (10), which attempts to maximize system accuracy while decreasing the size of the feature vector. Accuracy(i) denotes the system's accuracy as determined by Solution i, while S f (i) is how many features are chosen.…”

“…Calculate the opposite of elements to be modified in Solution i using Equation (3.3). Solution i and its inverse are measured using the objective function represented in the Equation Equation (10). Replace Solution i by its opposite if the latter is better in terms of objective function value.…”

A Novel Feature Selection Method Based on Bio-inspired Artificial Intelligence for palm vein authentication

“…In the first technique, the palm vein images are split into 5 overlapping sub-area and described using the Binarized Statistical Image Features descriptor method (BSIF), and in the second method, each palm vein image is descriptorized using a model that employs convolutional neural networks. Trabelsi et al [10] designed a uni-modal and multi-modal biometric systems that use feature selection and deep learning methods. The main objective of the proposed methodology termed PalmNet-Gabor is to accelerate the recognition of multi-spectral and contactless palmprint images.…”

Contactless Palmprint Recognition System: A Survey

“…However, this has had a detrimental effect on the advancement of palmprint recognition. As a result of these failures, contactless palmprint recognition has been developed to increase user-friendly and hygienic and safeguard user privacy [4]- [7]. Thus, this paper provides an overview and evaluation of the contactless palmprint recognition system, the state-of-the-art performance of existing works, different types of "Region of Interest" (ROI) extraction algorithms, feature extraction, and matching algorithms.…”