Real time embedded network

by barkkathulla 2012-09-20 16:25:13

<font color=#00FF1A><font color=#000066>Real-Time systems a</font>re becoming pervasive. Real-Time systems span several domains of computer science. Typical examples of real-time systems include Air Traffic Control Systems, Networked Multimedia Systems, Command Control Systems, defence and space systems, embedded automotive electronics etc. Real-Time systems are classified from a number of viewpoints i.e. on factors outside the computer system and factors inside the computer system.
In a real-time system the correctness of the system behaviour depend not only the on logical results of the computations, but also on the physical instant at which these results are produced. A real-time computer system must react to stimuli from the controlled object or the operator within time intervals dictated by its environment.
The instant at which a result is produced is called a deadline. If the result has utility even after the deadline has passed, the deadline is classified as soft, otherwise it is firm. If a catastrophe could result if a firm deadline is missed, the deadline is hard. Commands and Control systems, Air traffic control systems are examples for hard real-time systems. On-line transaction systems, airline reservation systems are soft real-time systems.

1.2 Introduction to Face Recognition
In recent years, face recognition has attracted much attention and its research has rapidly expanded by not only engineers but also neuroscientists, since it has many potential applications in computer vision communication and automatic access control system. Especially, face detection is an important part of face recognition as the first step of automatic face recognition.

However, face detection is not straightforward because it has lots of variations of image appearance, such as pose variations, occlusion, image orientation, illuminating condition and facial expression. Many novel methods have been proposed to resolve each variation listed above.

The feature invariant approaches are used for feature detection of eyes, mouth, ears, nose, etc. The appearance-based methods are used for face detection with Eigen face, neural network and information theoretical approach. Nevertheless, implementing the methods altogether is still a great challenge.

Fortunately, the images used in this project have some degree of uniformity thus the detection algorithm can be simpler because all the faces used here are vertical and have frontal view; second, they are under almost the same illuminate condition.

The Principal Component Analysis (PCA) algorithm method for feature extraction is used in our project with one of the distance classifier. The Eigen faces of original face images are considered for the processing involved in the face detection.
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