The project is focused on facial detection and recognition resilient to real-world alterations such as changing scale, head rotation or occlusion. They are important tasks in many applications from human computer interaction to biometrics and forensics. Although these tasks are easily solved by humans, current Computer Vision algorithms struggle in the presence of such alterations. One goal is to contribute with methods to reliably detect facial landmarks with symmetry filters, which have been successfully applied to detect eyes in controlled conditions. This contrasts with existing algorithms, where face is modeled holistically and detected globally, thus failing in case of perturbations. For recognition, we will use Gabor filters on retinotopic sampling grids. Such a grid models the photoreceptors in the retina, with sampling frequency decreasing exponentially from the center to the periphery. Gabor filters mimic the functioning of cells of the visual cortex having different spatial directions and frequencies. This approach has already shown high discrimination power using face in controlled environments. These innovations will contribute to increase user convenience and comfort, reducing the cooperation level required and allowing the use of their own device and natural interaction patterns to communicate with digital systems. It will enable the use of facial data acquired in a wide range of operational conditions due to the large amount of cameras already available.