Face and eye detection by CNN algorithms

David Balya, T. Roska

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

A novel approach to critical parts of face detection problems is given, based on analogic cellular neural network (CNN) algorithms. The proposed CNN algorithms find and help to normalize human faces effectively while their time requirement is a fraction of the previously used methods. The algorithm starts with the detection of heads on color pictures using deviations in color and structure of the human face and that of the background. By normalizing the distance and position of the reference points, all faces should be transformed into the same size and position. For normalization, eyes serve as points of reference. Other CNN algorithm finds the eyes on any grayscale image by searching characteristic features of the eyes and eye sockets. Tests made on a standard database show that the algorithm works very fast and it is reliable.

Original languageEnglish
Pages (from-to)497-511
Number of pages15
JournalJournal of VLSI Signal Processing Systems for Signal, Image, and Video Technology
Volume23
Issue number2
Publication statusPublished - 1999

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Cellular neural networks
Network Algorithms
Cellular Networks
Face
Neural Networks
Normalize
Face Detection
Reference Point
Normalization
Color
Deviation
Face recognition
Requirements
Human

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Face and eye detection by CNN algorithms. / Balya, David; Roska, T.

In: Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, Vol. 23, No. 2, 1999, p. 497-511.

Research output: Contribution to journalArticle

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