Local Gradient Increasing Pattern (LGIP) for Facial Representation and Gender Recognition

  • Lu Bing Zhou
  • Han Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)


A robust facial representation is an essential component for gender classification. This paper introduces a new local feature, Local Gradient Increasing Pattern (LGIP), which expresses the local intensity increasing trend. A LGIP feature is to encode intensity increasing trends in 8 orientations at each pixel using signs of directional gradient responses, and overall increasing trend is assigned with a decimal label. A facial image is partitioned into overlapping regions from which LGIP histograms are obtained and concatenated into a single feature vector. Gender classification is carried out using SVM classifier based on the LGIP-based facial descriptor. We investigate the influence to recognition rates by two factors, image resolution and person-dependent/independent condition. Experiments are performed on two replicable image sets from CAS-PEAL and FERET databases, and the results show that our method achieves better performance than many other methods.


gender classification local gradient increasing pattern facial representation support vector machine 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lu Bing Zhou
    • 1
  • Han Wang
    • 1
  1. 1.School of Electrical & Electronic EngineeringNanyang Technological UniversitySingaporeSingapore

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