**6. Conclusion**

*Visual Impairment and Blindness - What We Know and What We Have to Know*

airplane indicators.

to perform the recognition task.

related to translation and rotation.

**5.1 Detection and recognition of dynamic shapes**

shape and make the recognition task more complicated.

**5.2 Representation and description of planar shapes**

characteristic vectors are generally compact.

3.Aviation: the flight simulators track the pilot eye and head movement in order to analyze the pilot's behavior under realistic circumstances. This simulator is capable of evaluating a pilot's performance based on his eye movements combined with other information. It can be also used as an important training tool for new pilots in order to help them to look at the primary flight display (PFD) more regularly in order to monitor different

Detection of dynamic forms is a very important research area that is rapidly evolving in the field of image processing. The goal is to recognize the shapes of objects in an image or in a sequence of images from the information relating to their shapes. In fact, shape is one of the most differentiating features in an image. However, the description and representation of an image remain a major challenge

The quality of a descriptor is represented by its intelligence and ability to distinguish the different forms in a reliable manner despite the geometric variations

On the other hand, a reliable descriptor must withstand the various changes that affect the shape of an object such as noise and distortion that can actually alter the

The form representation and description techniques can be generally split into two main classes of methods: contour-based methods and region-based methods. This ranking depends on how the shape features are extracted: from only the outline or the entire region of the shape. For each category, the different approaches are divided into global approaches and local (structural) approaches. This subclassification is based on the representation of the form that depends on the whole form or parts of the form (primitives). These approaches can also be distinguished according to the spatial or transform processing space, in which the shape characteristics are calculated. Global methods are not always robust against occlusions and image noise. In addition, they require an entire and correct segmentation of objects in the images. In general, the segmentation process results in partitioning objects into regions or contour parts that do not necessarily correspond to whole objects.

The contour-based approaches only exploit the boundary of the object for the characterization of the form by ignoring its inner content. The most commonly used representation in contour-based recognition methods is the signature of the form [4]. For a given form, the signature is essentially a representation based on the parameters 1D of the contour of shape. This can be done using a scalar value of the radial distance, angle, curvature, or velocity function. Let us note here that the signature of an entire form (closed curve) is often a periodic function; this will not be the case of a part of form (open curve) for which the two ends are not contiguous. Outline-based descriptors include Fourier descriptors [5, 6], the wavelet descriptors [7, 8], the multi-scale curvature [9], the shape context [10], the contour moments [11], and the symbol chain [12, 13]. Since these descriptors are calculated using only the pixels of the contour, the computational complexity is low, and their

In region-based approaches, all pixels of the object are considered for characterization of the shape. This type of methods aims to exploit not only the information of the shape boundary but also that of the inner region of the form. The majority of

**356**

In this chapter, we presented different biometric techniques used in the industrial world as well as their performances.

We started with an overview of biometric systems as well as an overview of biometrics. Then we presented the different issues and challenges related to implementation of such systems.

After that, we presented a performance evaluation of different biometric systems given the issues and challenges previously stated. Then we presented an overview of some important biometric elements such as the databases and the degree of confidence. Furthermore, a detailed analysis of different domains of application of several biometric techniques was presented with a focus on eye movement tracking techniques.

Finally, the different approaches of recognition of dynamic and planar shapes were discussed in the last paragraph.
