Neural network based face detection from pre-scanned and row-column decomposed average face image

Authors:
Ziya Telatar;Murat H. Sazli;Irfan Muhammad
Affiliations:
Ankara University, Faculty of Engineering, Electronics Engineering Department, Ankara, Turkey;Ankara University, Faculty of Engineering, Electronics Engineering Department, Ankara, Turkey;Ankara University, Faculty of Engineering, Electronics Engineering Department, Ankara, Turkey
Venue:
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Year:
2007

Citing 7
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Abstract

This paper introduces a methodology for detecting human faces with minimum constraints on the properties of the photograph and appearance of faces. The proposed method uses average face model to save the computation time required for training process. The average face is decomposed into row and column sub-matrices and then presented to the neural network. To reduce the time required for scanning the images at places where the probability of face is very low, a pre-scan algorithm is applied. The algorithm searches the faces in the image at different scales for detecting faces in different sizes. Arbitration between multiple scales and heuristics improves the accuracy of the algorithm. Experimental results are presented in this paper to illustrate the performance of the algorithm including accuracy and speed in detecting faces.