An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images

Abstract

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used.

Deep Residual Network-Based Recognition of Finger Wrinkles Using Smartphone Camera

“…A study by [22] proposed the palmprint recognition method based on binary wavelet transform and local binary pattern (LBP). Gumaei et al proposed a method of palmprint recognition using visible and multispectral sensor images based on histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) [23]. Kumar et al proposed an approach for matching contactless palmprint images using accurate deformation alignment and matching [24].…”

Democratic voting downsampling for coding‐based palmprint recognition

“…Gumaei et al [19] proposed an efficient normalised Gist descriptor for palmprint feature extraction and used an optimised autoencoder to reduce feature dimensionality. They also employed the optimised autoencoder to reduce the dimensionality of the hybrid features to improve robustness, accuracy and efficiency of palmprint recognition, which were extracted from the histogram of oriented gradients and a steerable Gaussian filter [20].…”

Improving Palmprint based Biometric System Performance using Novel Multispectral Image Fusion Scheme

“…Using discriminant kernel analysis (KDA) a dimension reduction was performed before classification using a classifier (SRC). A new approach to recognition of palm prints has been presented in [35].This approach is based on AE (an optimized auto-encoder) (AE), RELM (regularized extreme learning machine) and an extraction method (HOG-SGF). The experiments were carried out on three databases (CASIA, MS-PolyU, and Tongji of contact less visible palmprint images).…”