FACE RECOGNITOIN TECHNIQUE<br />REVIEW-1<br />By:Suvigya Tripathi (09BEC094)<br /> Ankit V. Gupta (09BEC106)<br />Guided By:Prof. Bhupendra Fataniya<br />Dept. of Electronics and Communication Engineering,<br />Nirma University at Ahmedabad.<br />
<ul><li>It uses human-coded rules to model facial features, such as two symmetric eyes, a nose in the middle and a mouth underneath the nose.</li></ul>KNOWLEDGE-BASED APPROACH<br />
<ul><li>Pros: </li></ul>Easy to come up with simple rules <br />Based on the coded rules, facial features in an input image are extracted first, and face candidates are identified <br />Work well for face localization in uncluttered background <br /><ul><li>Cons:</li></ul>Difficult to translate human knowledge into rules precisely: detailed rules fail to detect faces and general rules may find many false positives <br />Difficult to extend this approach to detect faces in different poses: implausible to enumerate all the possible cases<br />KNOWLEDGE-BASED APPROACH-SUMMARY<br />
<ul><li>Feature invariant methods try to find facial features which are invariant to pose, lighting condition or rotation.
Skin colors, edges and shapes fall into this category.</li></ul>FEATURE INVARIANT METHOD<br />
FEATURE INVARIANT METHOD-NODAL POINT ANALYSIS<br /><ul><li>Every face has numerous, distinguishable landmarks, the different peaks and valleys that make up the face
The length of the jaw line</li></li></ul><li><ul><li>Pros: </li></ul>Features are invariant to pose and change in orientation.<br /><ul><li>Cons: </li></ul>Difficult to locate facial features due to several corruption (illumination, noise, occlusion) <br />Difficult to detect features in complex background<br />FEATURE INVARIANT METHOD-SUMMARY<br />
<ul><li>Template matching methods calculate the correlation between a test image and a pre-selected facial templates.</li></ul>TEMPLATE MATCHING METHOD<br />
Pros: <br />Simple <br />Cons: <br />Templates needs to be initialized near the face images <br />Difficult to enumerate templates for different poses (similar to knowledge-based methods)<br />TEMPLATE MATCHING METHOD-SUMMARY<br />
<ul><li>Using skin color to find face segments is a vulnerable technique.
Non-animate objects with </li></ul>the same color as skin can <br />be picked up since the <br />technique uses color <br />segmentation.<br /><ul><li>Then the face can be picked up using any of the approaches.</li></ul>BIOMETRICSSKIN TEXTURE ANALYSIS<br />
<ul><li>Lack of restriction to orientation or size of faces.
A good algorithm can handle complex backgrounds.
It is relatively insensitive to changes in expression, including blinking, frowning or smiling
Has the ability to compensate for mustache or beard growth and the appearance of eyeglasses.</li></ul>SKIN TEXTURE ANALYSIS:ADVANTAGES<br />
Security measure at ATM’s<br />Digital Cameras<br />Public Surveillance (CCTV’s) at <br />Airports, Hospitals, etc.<br />Televisions and computers can <br />save energy by reducing the <br />brightness.<br />APPLICATIONS<br />