Object detection is a computer vision technique that identifies objects in images and videos. It can detect things like faces, humans, buildings, and cars. Object detection has applications in areas like image retrieval, video surveillance, and face detection. Image processing techniques are used to both improve images for human interpretation and to make images more suitable for machine perception. These techniques include enhancing edges, converting images to binary, greyscale, or true color formats. Face detection is a common application that finds faces in images and ignores other objects. It is often used as the first step in face recognition systems.

2. • Object detection is a computer technology related
to Computer vision and image processing
• That deals with detecting instances of semantic
objects of a certain class (such as humans,
buildings, or cars) in digital images and videos.
• Well-researched domains of object detection
include face detection and pedestrian detection,
Object detection has applications in many areas of
computer vision ,including image
retrieval and video surveillance.

3. • What is image processing :
I. Improve its pictorial information for human interpretation
II. Render it more suitable for autonomous machine
perception.

4. • It is necessary to realize that these two aspects represent
two separate but equally important aspects of image
processing. A procedure which satisfies condition:
I. A procedure which makes an imagelook better may be the
very worst procedure for satisfying condition.
II. Humans like their images to be sharp, clear and detailed;
machines prefer their images to be simple and uncluttered.
Examples (1) may include:

5. Enhancing the edges of an image to make it appear sharper; an example is shown in figure 1.1.
Note how the second image appears cleaner it is a more pleasant image. Sharpening edges
is a vital component of printing: in order for an image to appear at its best on the printed
page some sharpening is usually performed.

6. BINARY:
• Each pixel is just black or white. Since there are
only two possible values for each pixel.
• we only need one bit per pixel. Such images can
therefore be very efficient in terms of storage.
• Images for which a binary representation may be
suitable include text (printed or Handwriting)
An example was the image shown in figure below :-

7. In this image, we have only the two colors:
White 1 for the edges, and black 0for the background

9. GREYSCALE :
• Each pixel is a shade of grey, normally from (0)
black to(255) white.
• This range means that each pixel can be
represented by eight bits, or exactly one byte.
• Other greyscale ranges are used, but generally they
are a power of 2. Such images arise in medicine (X-
rays).
• images of printed works, and indeed 256 different
grey levels is sufficient for the recognition of most
natural objects.

11. • 3- TRUE COLOR, OR RGB :
• each pixel has a particular color; that color being
described by the amount of red, green and blue in.
• This means that for every pixel there correspond
three values.
• total number of bits required for each pixel is 24
such image are also called 24 -bit color images.

13. ** Some Cameras depending on human’s mood , such Smile Shutter ® in Sony Cyber-shot
Cameras

14. • Human moods with Face detection technologies Is
also a computer technology that determines the
locations and sizes of human faces in arbitrary
(digital) images. It detects facial features and
ignores anything else, such as buildings, trees and
bodies.
Next Figure shows how 

16. • Face recognition system is an application to verify a
person's identity by comparing pictures taken
recently from a digital camera or VCR, and
compare with the images stored in the system
database.
• Face recognition system has been widely used in
recent years for security and access control, like
other security systems that rely on biometrics such
as fingerprint or eye IRIS.

17. As face detection is the first step of any face
processing system, it finds numerous
applications in face recognition, face tracking,
facial expression recognition, facial feature
extraction, gender classification, clustering,
attentive user interfaces, digital cosmetics,
biometric systems. In addition, most of the
face detection algorithms can be extended to
recognize other objects such as cars, humans,
pedestrians, and signs, etc

19. Japanese male , Italian ,Old-Man , Japanese female , Non-Japanese male , Black male

21. • Each face has several distinct properties, is the
different curves on the face. And based on this
technology recognize faces as landmarks. Each face has
approximately 80 knots and months those nodes
which can be measured using software are:
• (1) the distance between the eyes.
• (2) presentation of the nose.
• (3) the depth of the eye.
• (4) the form cheekbones.
• (5) the length of the jaw line.
• These parameters are measured by the program
specialist for face recognition and translated into
numerical codes called face recognition face print and
used to represent the face in the database.

23. • Recognize these systems to people depending on their
photos, and unlike the old identification systems, these
systems give an alert to the presence of undesirable
persons and not only verify the identity and this
greatly supports where security can be used in the
publication of photographs of criminals in public places
in order to identify them, at airports and seaports to
search for personal, assumed by the Immigration
Department to search for retarded and outlaws, In the
playground to find troublemakers (used in USA), voting
(used by the Mexican Government in 2000 m),
recording observations on the street (in England), the
system BIOS licenses (in the State of Illinois in
America).

25. • Pre-Crime Face Scanner to Be Used For ‘Security Interrogations’
• “A sophisticated new camera system can detect lies just by watching
our faces as we talk, experts say. The computerized system uses a
simple video camera, a high-resolution thermal imaging sensor and a
suite of algorithms,”
• The new system “successfully discriminates between truth and lies in
about two-thirds of cases,” which equates to little more accuracy than
chance alone, making it even less reliable than the notorious polygraph
test, which has been widely discredited and is habitually inaccurate.
The technology is focused around detecting emotions such as distress,
fear or distrust, all of which a stressed traveler could undergo without
necessarily being a liar. Indeed, such emotions would be expected in an
environment where people are being naked body scanned, groped by
TSA thugs, and subjected to lie detector interrogations.

27. • Cons faces discrimination technique
• Despite the success of these systems and their
evolution, but they do not reach perfect and there are
some factors that may impede the process of face
recognition and these obstacles:
• (1) solar glasses.
• (2) long hair masks the central part of the face.
• (3) lighting dimming that results in blurred images.
• (4) poor accuracy and clarity of images taken.
• (5) changes in physiological characteristics in the face
of either age or other.
• (6) changes in the working environment reduce
matching accuracy.