Palmprint identification algorithm using Hu invariant moments and Otsu binarization

Abstract

Recently, Biometrics-based personal identification is regarded as an effective method of person's identity with recognition automation and high performance. In this paper, the palmprint recognition method based on Hu invariant moment is proposed. And the lowresolution(75dpi) palmprint image(135×135 Pixel) is used for the small scale database of the effectual palmprint recognition system. The proposed system is consists of two parts: firstly, the palmprint fixed equipment for the acquisition of the correctly palmprint image and secondly, the algorithm of the efficient processing for the palmprint recognition. And the palmprint identification step is limited 3 times. As a results, when the coefficient is 0.001 then FAR and GAR are 0.038% and 98.1% each other. The authors confirmed that FAR is improved 0.002% and GAR is 0.1% each other compared with online palmprint Identification algorithm..

Biometric Person Identification System: A Multimodal Approach Employing Spectral Graph Characteristics of Hand Geometry and Palmprint

“…144 users compared to other works i.e. 50 users in [14,15] and 100 users in [13,27]. The correct identification rate of 99.19% with 46 number of features for the proposed multimodal biometrics system is achieved with multi-class SVM classifier.…”

“…The central moment relationships u p to the level three are used for computations of Hu invariant moments. The scale invariance is obtained by normalization [27].…”

“…The performance of the proposed multimodal biometric system with feature level fusion of hand geometry and palmprint traits is compared with the four previously reported similar works as shown in Table III. In previous works [13][14][15]27] the number of features extracted are more compared to the proposed method. The geometrical features [13,14,27] and chain code features [15] are extracted from the handgeometry trait.…”

“…In previous works [13][14][15]27] the number of features extracted are more compared to the proposed method. The geometrical features [13,14,27] and chain code features [15] are extracted from the handgeometry trait. whereas DCT feature [13], Gabor feature [14], chain code feature [15] and line feature [27] are extracted from the palmprint trait.…”

Automatic Personal Identification Using Feature Similarity Index Matching

“…A palm print contains distinctive features such as principal lines, wrinkles, ridges and valleys on the surface of the palm. Palmprint has abundant lines and ridge structure, which can be used for matching Cheng and Moon et al (2008); Noh and Rhee (2005); Laadjel et al (2008) and Sarhan (2009). The problem of palmprint-based personal identification can be described as follows: given an example palmprint, compare it with all of the possible candidates in the database to determine whether the queried example and the candidates are from the same palm.…”