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.
Highlights
The fast growth of modern human civilization has led to an increasing demand for new and efficient technologies to sustain it
We proposed efficient unimodal and multimodal identification systems for fast palmprint recognition
We used feature selection methods to select a subset of relevant features of PalmNet using Fisher score and ReliefF methods and dimensionality reduction by Whitening Principal Component Analysis (WPCA) method to reduce the computational time and improve the accuracy recognition
Summary
The fast growth of modern human civilization has led to an increasing demand for new and efficient technologies to sustain it. Biometric technologies focus on techniques that automatically authenticate both stable human traits, such as DNA, fingerprint [1], faces [2], iris [3], palmprint, and human behavioral traits such as gait [4], voice [5], Extended author information available on the last page of the article keystroke [6], and signature [7]. The palmprint images contain rich features such as principal lines, wrinkles, and minutiae. They are relatively stable, and their captured images are easy to obtain [8, 9]. They can be categorized according to the way of their acquisition. They can be divided into two categories of palmprint images, contact-based and contactless. The first type of image is gathered by placing the palms on the device and using user-pegs, while the second type is obtained without contacting the device’s surface [11]
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