Face Recognition by Kresimir Delac, Mislav Grgic (Editors)

By Kresimir Delac, Mislav Grgic (Editors)

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Figure 15. Eye points search: a) eye subimage b) edge image c) eye map d) initial template position e) final template position f ) fiducial points Figure 16. Examples of nose processing. 3 Mouth Regarding the mouth, our goal is to locate its corners and its upper and lower mid-points. To this aim, we use a snake [Hamarneh, 2000] to determine the entire contour since we verified that they can robustly describe the very different shapes that mouths can assume. To make the snake converge, its initialization is fundamental; therefore the algorithm estimates the mouth corners and anchors the snake to them: first, we represent the mouth subimage in the YCbCr color space, and we apply the following transformation: MM = (255 - (Cr - Cb)) Cr 2 Automatic Facial Feature Extraction for Face Recognition 49 MM is a mouth map that highlights the region corresponding to the lips; MM is then binarized putting to 1 the 20% of its highest values; the mouth corners are determined taking the most lateral extremes (see Figure 17).

2005]. However some techniques may require more features than just the eyes. g. , 1999] is based on the comparison of the image texture found in the neighborhood of several fiducial points. Due to these considerations, the performance evaluation of a feature extraction method is usually given in terms of error measures that take into account only the localized eye positions. In Sec. 3. we will motivate the choice of such measures and we will introduce the study of the recognition rate in function of the eye localization precision.

Comparing face images using the modified Hausdorff distance. Pattern Recognition, 31(12):1873-1881,1998. [18] Toshiba. Facepass. www. toshiba. co. jp/mmlab/tech/w31e. htm. [19] P. Viola and M. Jones. Robust real-time face detection. International Journal of Computer Vision (IJCV), 57(2): 137-154, 2004. [20] 30 Face Recognition H. Wang, S. Z. Li, and Y. Wang. Face recognition under varying lighting conditions using self quotient image. In Proc. IEEE International Conference on Automatic Face and Gesture Recognition (FOR), pages 819-824, 2004.

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