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 focussed on identification from full frontal/profile facial images. This paper conducts a first 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 data acquisition process. Our database consists of imagery corresponding to 150 unique subjects totaling to 3,000 facial images with 5 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.