Face authentication from cell phone camera images with illumination and temporal variations

We investigated the performance of three face verification algorithms (correlation filters, Individual PCA, and FisherFaces) on an image database collected by a cell phone camera. Cell phone camera images tend to be of poorer quality along with experiencing scale and dynamic illumination changes due to cell phone portability. While Individual PCA and FisherFaces work in the image domain, correlation filters work in the frequency domain and offer advantages such as shift-invariance, the ability to accommodate in-class image variability, and closed-form expressions. Verification results suggest that, with this database, correlation filters can offer a better performance than Individual PCA and comparable performance with FisherFaces with fewer filters.