Meena, K., Suruliandi, A. and Reena Rose, R. (2015) Enhancing the Performance of Texture-Based Face Recognition through Multi-Resolution Techniques. International Journal of Biometrics, 6, 363-386.
http://dx.doi.org/10.1504/IJBM.2014.067132

has been cited by the following article:

• TITLE: Local Binary Patterns and Its Variants for Finger Knuckle Print Recognition in Multi-Resolution Domain

  AUTHORS: D. R. Arun, C. Christopher Columbus, K. Meena

  KEYWORDS: Biometrics, Finger Knuckle Print, Contourlet Transform, Local Binary Pattern (LBP), Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP), Local Texture Description Framework Based Modified Local Directional Pattern (LTDF_MLDN), Nearest Neighbor (NN) Classifier, Extreme Learning Machine (ELM) Classifier

  JOURNAL NAME: Circuits and Systems, Vol.7 No.10, August 22, 2016

  ABSTRACT: Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier.