Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

Abstract

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases,

Geometrical Local Image Descriptors for Palmprint Recognition

“…(1-3). It shows that, compared to other feature extraction methods [7,8,9,14,15,16,17,23,25], the experimental method provides a superior performance, with an EER from 0 to 2.06. The performance of the method applied on the PolyU PPDB is presented in Table 1.…”

“…Single-hidden layer feedforward neural networks have often been learnt by utilizing ELM [17]. The iterative tuning of obscured nodes is not necessary after random initialization using ELM has been performed.…”

“…The probe samples are sorted so that they are associated with the class to which they are most similar. For this study, based on our previous work [17] and to validate our identification algorithms we have calculated the Recognition Rate. For PolyU (352*10*2 images) we have taken 10 images of one user for training and remaining 10 for testing.…”

“…A double-orientation code based on Gabor filters and nonlinear matching scheme was described in [15]. The methods presented in [15,16] were evaluated using multispectral palmprints from the MS-PolyU database, a proposed method for palmprint recognition (PPR) based on BSIF and Histogram of oriented gradients (HOG) [17]. The palmprint recognition scheme presented in this study combines a novel spiral feature extraction with a sparse representation of binarized statistical image features (BSIF).…”

“…The main contributions of this paper are summarized as follows : [14], palmcode [8], fusion code [9], the Gabor transform with KDA [7], A double-orientation code [15][16], BSIF and HOG [17] and the Gabor transform with sparse representation [23]. The rest of this paper is structured as follows.…”