Compression of personal identification pictures using vector quantization with facial feature correction

Jian Hong Hu, Ru Shang Wang, Yao Wang

Research output: Contribution to journalArticle


This paper describes a feature-correction two-stage vector quantization (FC2VQ) algorithm for the compression of photo ID pictures. The FC2VQ method treats different regions in a facial image differently. A region of facial features (ROFF), containing the eyes and the mouth, is detected and rendered more accurately than the rest of the image. The technique can compress a 128 ×128×8-bit (16,384 bytes total) ID image to an average size of 350 bytes. The quality of the compressed images is far superior to that obtained by other methods, including the JPEG standard, at similar compression ratios.

Original languageEnglish (US)
Pages (from-to)198-203
Number of pages6
JournalOptical Engineering
Issue number1
StatePublished - Jan 1996



  • Facial feature detection
  • Facial image compression
  • Vector quantization
  • Visual communications and image processing

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering(all)

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