Examples of Computer Vision Systems Applications Based on Neural Networks

Face recognition is an important security task. We propose a high-level method to solve this problem: a permutation coding neural classifier (PCNC). A PCNC with a special feature extractor for face image recognition systems is a relatively new method that has been tested with good results to classify real environment images (such as larvae of various types and hand-made elements). As baseline methods, a support vector machine (SVM) and the iterative closest point (ICP) method are selected for comparison. We applied these methods to gray-level images from the FRAV3D, FEI and LWF (Labeled Faces in the Wild) face databases. We aggregated various distortions for the initial images to improve the PCNC. We analyze and discuss the obtained results. For LWF database we have investigated experimentally three different cases. In the first, the recognition process was based on images of the whole faces. In the second case, the recognition process was based on fragment (eye-eyebrow) images. In the third case, the recognition process was based on fragment (mouth-chin) images. The results are presented. We describe recognition system for the Colorado potato beetles based on RSC neural classifier.