Research Article Open Access

Intelligent Sensor for Image Control Point of Eigenfaces for Face Recognition

Mohamed Lamine Toure1 and Zou Beiji2
  • 1 , Afganistan
  • 2 ,
Journal of Computer Science
Volume 6 No. 5, 2010, 484-491

DOI: https://doi.org/10.3844/jcssp.2010.484.491

Submitted On: 28 March 2010 Published On: 31 May 2010

How to Cite: Toure, M. L. & Beiji, Z. (2010). Intelligent Sensor for Image Control Point of Eigenfaces for Face Recognition. Journal of Computer Science, 6(5), 484-491. https://doi.org/10.3844/jcssp.2010.484.491

Abstract

Problem statement: The sensor for image control point in Face Recognition (FR) is one of the most active research areas in computer vision and pattern recognition. Its practical application includes forensic identification, access control and human computer interface. The task of a FR system is to compare an input face image against a database containing a set of face samples with known identity and identifying the subject to which the input face belongs. However, a straightforward implementation is difficult since faces exhibit significant variations in appearance due to acquisition, illuminations, pose and aging variations. This research contracted with several images combined through image registration offering the possibility of improving eigenface recognition. Sensor detection by head orientation for image control point of the training sets collected in a database was discussed. Approach: In fact, the aim of such a research consisted first, identification of the face recognition and the possibility of improving eigenface recognition. So the approach of eigenface focused on three fundamental points: generating eigenfaces, classification and identification and the method used image processing toolbox to perform the matrix calculations. Results: Observation showed that the performance of the proposed technique proved to be less affected by registration errors. Conclusion/Recommendations: We presented the intelligent sensor for face recognition using image control point of eigenfaces. It is important to note that many applications of face recognition do not require perfect identification, although most require a low false-positive rate. In searching a large database of faces, for example, it may be preferable to find a small set of likely matches to present to the user.

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Keywords

  • Sensor
  • database identification
  • face recognition
  • eigenface
  • spatial transformation