Pose Discriminiation and Eye Detection Using Support Vector Machines (SVM)

Most face recognition systems assume that the geometry of the image formation process is frontal. If additional poses, beyond the frontal one, are possible, then it becomes necessary to estimate the actual imaging pose. Once a face is detected and its pose is estimated one proceeds by normalizing the face images to account for geometrical and illumination changes, possibly using information about the location and appearance of facial landmarks such as the eyes. This paper describes a novel approach for the problem of pose estimation and eye detection using Support Vector Machines (SVM). Experimental results using frontal, and 33.75o rotated left and right poses, respectively, demonstrate the feasibility of our approach for pose estimation. The image (face) data comes from the standard FERET data base, the training set consists of 150 images equally distributed among frontal, 33.75o rotated left and right poses, respectively, and the test set consists of 450 images again equally distributed among the three different types of poses. The accuracy observed on test data, using both polynomials of degree 3 and Radial Basis Functions (RBFs) as kernel approximation functions, to determine the SVM separating hyperplanes, has been 100%. On the eye detection task, the training data consisted of 186 eye images and 186 non-eye images. SVM was tested against 200 test examples (eye and non-eye). The best generalization performance of 4% was achieved using polynomial kernels of second degree as the set of approximating functions. SVM appear to be robust classification schemes and this suggests their use for additional face recognition tasks, such as surveillance.