Abstract
Automatic palmprint identification has received much attention in security applications and law enforcement. The performance of a palmprint identification system is improved by means of feature extraction and classification. Feature extraction methods such as Subspace learning are highly sensitive to the rotation variances, translation and illumination in image identification. Thus, Histogram of Oriented Lines (HOL) has not obtained promising performance for palmprint recognition so far. In this paper, we propose a new descriptor of palmprint named Improved Histogram of Oriented Lines (IHOL), which is an alternative of HOL. Improved HOL is not very sensitive to changes of translation and illumination, and has the robustness against small transformations whereas the small translation and rotations make no change in histogram value adjustment of the proposed work. The experiment results show that based on IHOL, with Principal Component Analysis (PCA) subspace learning can achieve high recognition rates. The proposed method (IHOL-Cosine distance) improves 1.30% on PolyU I database, and similarly (IHOL-Euclidean distance) improves 2.36% on COEP database compared with existing HOL method.
Highlights
As a complementary technology for personal authentication methods, biometric identification is emerging as a powerful means for automatically recognizing identities
Feature vector size obtained from improved Histogram of Oriented Lines (HOL) algorithm on the PolyU I database is 1 × 8100 features
By means of dimensionality reduction technique Principal Component Analysis (PCA), the feature vector size is reduced to different extent
Summary
As a complementary technology for personal authentication methods, biometric identification is emerging as a powerful means for automatically recognizing identities. The coding based approaches like line feature [3], feature points [4], Fourier spectrum [5], Eigen palm feature [6], texture energy [7], wavelet signature [8], gabor phase [9], competitive code [10], statistical line based [11], palm code etc. It has been solved by using ordinal measure for common print representation [12]
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