  This paper proposes a new technique for automatic face recognition using
integrated peaks of the Hough transformed significant blocks of the binary
gradient image. In this approach firstly the gradient of an image is calculated
and a threshold is set to obtain a binary gradient image, which is less
sensitive to noise and illumination changes. Secondly, significant blocks are
extracted from the absolute gradient image, to extract pertinent information
with the idea of dimension reduction. Finally the best fitted Hough peaks are
extracted from the Hough transformed significant blocks for efficient face
recognition. Then these Hough peaks are concatenated together, which are used
as feature in classification process. The efficiency of the proposed method is
demonstrated by the experiment on 1100 images from the FRAV2D face database,
2200 images from the FERET database, where the images vary in pose, expression,
illumination and scale and 400 images from the ORL face database, where the
images slightly vary in pose. Our method has shown 93.3%, 88.5% and 99%
recognition accuracy for the FRAV2D, FERET and the ORL database respectively.
