Eye features normalization and face emotion detection for human face recognition

Iris recognition is very essential in human identification. It gets more attention in human face recognition. There are several proposals to develop systems that operate in the visible wavelength and in less constrained environments. These imaging conditions engender acquired noisy artefacts that lead to severely degraded images, making iris segmentation a major issue. Having observed that existing iris segmentation methods tend to fail in these challenging conditions, we present a segmentation method that can handle degraded images acquired in less constrained conditions. we offer the following contributions: first to consider the sclera the most easily distinguishable part of the eye in degraded images, then a new type of feature that measures the proportion of sclera in each direction and is fundamental in segmenting the iris, and finally to run the entire procedure in deterministically linear time in respect to the size of the image, making the procedure suitable for real-time applications. In this paper we discussed eye features normalisation and face detection for human identification.