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The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face. This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences. Using the proper PIN gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes. Despite warning, many people continue to choose easily guessed PINâ„¢s and passwords: birthdays, phone numbers and social security numbers. Recent cases of identity theft have highten the need for methods to prove that someone is truly who he/she claims to be. Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins. Its nontransferable. The system can then compare scans to records stored in a central or local database
Face recognition technology
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seminar about face recognition based on PCA, By Zeena Mohammed, University of Technology
face recognition based on PCA
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PCA, commonly referred to as the use of eigenfaces, is the technique pioneered by Kirby and Sirivich in 1988. With PCA, the Unknown and Known images must be the same size. The PCA approach is used to reduce the dimension of the data by means of data compression basics and reveals the most effective low dimensional structure of facial patterns. This reduction in dimensions removes information that is not useful and precisely decomposes the face structure into orthogonal (uncorrelated) components known as eigenfaces. Each face image may be represented as a weighted sum (feature vector) of the eigenfaces, which are stored in a 1D array. An Unknown image is compared against a Known image by measuring the distance between their respective feature vectors.
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The information age is quickly revolutionizing the way transactions are completed. Everyday actions are increasingly being handled electronically, instead of with pencil and paper or face to face. This growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Access codes for buildings, banks accounts and computer systems often use PIN's for identification and security clearences. Using the proper PIN gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen, an unauthorized user can often come up with the correct personal codes. Despite warning, many people continue to choose easily guessed PINâ„¢s and passwords: birthdays, phone numbers and social security numbers. Recent cases of identity theft have highten the need for methods to prove that someone is truly who he/she claims to be. Face recognition technology may solve this problem since a face is undeniably connected to its owner expect in the case of identical twins. Its nontransferable. The system can then compare scans to records stored in a central or local database
Face recognition technology
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PCA, commonly referred to as the use of eigenfaces, is the technique pioneered by Kirby and Sirivich in 1988. With PCA, the Unknown and Known images must be the same size. The PCA approach is used to reduce the dimension of the data by means of data compression basics and reveals the most effective low dimensional structure of facial patterns. This reduction in dimensions removes information that is not useful and precisely decomposes the face structure into orthogonal (uncorrelated) components known as eigenfaces. Each face image may be represented as a weighted sum (feature vector) of the eigenfaces, which are stored in a 1D array. An Unknown image is compared against a Known image by measuring the distance between their respective feature vectors.
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Eigenfaces , Fisherfaces and Dimensionality Reduction are explained easily and clearly. These topics focus on 2 face recognition methods and explains the mathematics behind them. Hope it Helps!
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Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
MichaelStarkes_UncutGemsProjectSummary.pdf
MichaelStarkes_UncutGemsProjectSummary.pdf
An overview of the various scriptures in Hinduism
An overview of the various scriptures in Hinduism
PSYPACT- Practicing Over State Lines May 2024.pptx
PSYPACT- Practicing Over State Lines May 2024.pptx
philosophy and it's principles based on the life
philosophy and it's principles based on the life
....................Muslim-Law notes.pdf
....................Muslim-Law notes.pdf
II BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING II
II BIOSENSOR PRINCIPLE APPLICATIONS AND WORKING II
Software testing for project report .pdf
Software testing for project report .pdf
Eigenface For Face Recognition
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Eigenface for Face
Recognition Presenter: Trần Đức Minh
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