Autonomous facial recognition based on the human visual system

This paper presents a real-time facial recognition system utilizing our human visual system algorithms coupled with logarithm Logical Binary Pattern feature descriptors and our region weighted model. The architecture can quickly find and rank the closest matches of a test image to a database of stored images. There are many potential applications for this work, including homeland security applications such as identifying persons of interest and other robot vision applications such as search and rescue missions. This new method significantly improves the performance of the previous Local Binary Pattern method. For our prototype application, we supplied the system testing images and found their best matches in the database of training images. In addition, the results were further improved by weighting the contribution of the most distinctive facial features. The system evaluates and selects the best matching image using the chi-squared statistic.