An investigation into the use of partial-faces for face recognition

Even though numerous techniques for face recognition have been explored over the years, most research has primarily focused on identification from full frontal/profile facial images. This paper conducts a systemic study to assess the performance when using partial faces for identification. Our specific approach considers an ensemble of radial basis function (RBF) networks. A specific advantage of using an ensemble is its ability to cope with the inherent variability in the image formation and the data acquisition process. Our database consists of imagery corresponding to 150 unique subjects, totalling 3,000 facial images with /spl plusmn/5/spl deg/ rotation. Based on our experimental results, we observe that the average cross-validation performance is the same, even if only half the face image is used instead of the full-face image. Specifically, we obtain 96% when partial faces are used and 97% when full faces are used.