Comparative assessment of content-based face image retrieval in different color spaces

Peichung Shih, Chengjun Liu

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations

Abstract

Content-based face image retrieval is concerned with computer retrieval of face images (of a given subject) based on the geometric or statistical features automatically derived from these images. It is well known that color spaces provide powerful information for image indexing and retrieval by means of color invariants, color histogram, color texture, etc. This paper assesses comparatively the performance of content-based face image retrieval in different color spaces using a standard algorithm, the Principal Component Analysis (PCA), which has become a popular algorithm in the face recognition community. In particular, we comparatively assess 12 color spaces (RGB, HSV, YUV, YCbCr, XYZ, YIQ, L*a*b*. U*V*W*, L*u*v*, I1I2I3, HSI, and rgb) by evaluating 7 color configurations for every single color space. A color configuration is defined by an individual or a combination of color component images. Take the RGB color space as an example, possible color configurations are R, G, B, RG, RB, GB, and RGB. Experimental results using 1,800 FERET R, G, B images corresponding to 200 subjects show that some color configurations, such as R in the RGB color space and V in the HSV color space, help improve face retrieval performance.

Original languageEnglish (US)
Pages (from-to)1039-1048
Number of pages10
JournalLecture Notes in Computer Science
Volume3546
DOIs
StatePublished - 2005
Event5th International Conference on Audio - and Video-Based Biometric Person Authentication, AVBPA 2005 - Hilton Rye Town, NY, United States
Duration: Jul 20 2005Jul 22 2005

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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