- Advances in Biometrics for Secure Human Authentication and Recognition
- Biometric iris recognition using radial basis function neural network | izapolobalid.ml
- Soft Computing for Recognition Based on Biometrics
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Chen X An effective synchronization clustering algorithm. Appl Intell 46 1 — CrossRef. Daugman J High confidence personal identification by rapid video analysis of iris texture.click here
Advances in Biometrics for Secure Human Authentication and Recognition
In: International Carnahan conference on security technology, Crime countermeasures, proceedings. Institute of electrical and electronics engineers. IEEE, Washington, pp 50— Daugman JG High confidence visual recognition of persons by a test of statistical independence. Daugman JG U. Patent No. Washington, DC: U. Patent and Trademark Office.
Kluwer, Norwell. Daugman J Statistical richness of visual phase information: update on recognizing persons by iris patterns. Daugman J How iris recognition works. Daugman J New methods in iris recognition. Daugman J Information theory and the iris code. Galbally J, Marcel S, Fierrez J Image quality assessment for fake biometric detection: application to iris, fingerprints, and face recognition.
Gupta K, Gupta R Iris recognition system for smart environments. IEEE, Washington, pp 1—6. Gupta R, Gupta K Iris recognition using templates fusion with weighted majority voting. Haddouch K, Elmoutaoukil K, Ettaouil M Solving the weighted constraint satisfaction problems via the neural network approach. Kyaw KSS Iris recognition system using statistical features for biometric identification.
Biometric iris recognition using radial basis function neural network | izapolobalid.ml
In: international conference on electronic computer technology. Testing set. Real-time Examples and Explanations: A pattern is a physical object or an abstract notion. While talking about the classes of animals, a description of an animal would be a pattern. While talking about various types of balls, then a description of a ball is a pattern.
Soft Computing for Recognition Based on Biometrics
In the case balls considered as pattern, the classes could be football, cricket ball, table tennis ball etc. Given a new pattern, the class of the pattern is to be determined. The choice of attributes and representation of patterns is a very important step in pattern classification. A good representation is one which makes use of discriminating attributes and also reduces the computational burden in pattern classification.
An obvious representation of a pattern will be a vector. Each element of the vector can represent one attribute of the pattern. The first element of the vector will contain the value of the first attribute for the pattern being considered. Example: While representing spherical objects, 25, 1 may be represented as an spherical object with 25 units of weight and 1 unit diameter. The class label can form a part of the vector. If spherical objects belong to class 1, the vector would be 25, 1, 1 , where the first element represents the weight of the object, the second element, the diameter of the object and the third element represents the class of the object.
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Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below. Writing code in comment? Please use ide. What to expect? What is Pattern Recognition? In classification, an appropriate class label is assigned to a pattern based on an abstraction that is generated using a set of training patterns or domain knowledge.
Classification is used in supervised learning. The gait cycle is calculated using three consecutive local minima computed for the distance between left and right ankles. For Kinect face recognition, a novel method based on HOG features has been developed.
Then, K-nearest neighbors feature matching algorithm is applied as feature classification for both gait and face biometrics. Two fusion algorithms are implemented. The combination of Borda count and logistic regression approaches are used in the rank level fusion.
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The weighted sum method is used for score level fusion. Monwar M. Nandakumar K. Jain A.
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