Author Yednekachew Asfaw, Bryan Chen, Andy Adler Institution Sch. of Inf. Technol. & Eng., Ottawa Univ., Ont., Canada. Title Impact of Pose and Glasses on Face Detection using the Red Eye Effect Source Conference Information Canadian Conference on Electrical and Computer Engineering, Montreal, Canada, May 2003 Abstract In current image-processing algorithms for face detection performance is not completely reliable, especially in situations with variable lighting, and with low-resolution images. One possible approach to implement face detection is the use of the "redeye" effect: the reflection produced by human eyes when exposed to co-axial infrared (IR) light. We investigated the effectiveness of the red-eye technique for variability in: skin tone, eye color, pose, angle of IR illumination, scene illumination, and the effect of shine from glasses. Algorithms were developed to detect eye locations from a single IR image. Image processing steps involved: normalization, blurring, dynamic threshold calculation, and candidate eye position validation. Average eye position estimation accuracy approaches 80 to 85 percent.