| Type of Material: | Thesis |
| Title: | A Novel Approach for Face Recognition Technique Using Various Pattern Models and Enhancing their Performance |
| Researcher: | RAJAKUMARI, K |
| Guide: | NALINI, C |
| Department: | Department of Engineering and Technology(Computer Science and Engineering) |
| Publisher: | Bharath University, Chennai |
| Place: | Chennai |
| Year: | 2019 |
| Language: | English |
| Subject: | Computer Science | Computer Science Theory and Methods | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai; 2019; D13CS005 |
| Fulltext: | Shodhganga |
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| 040 | __ | |aBHAU_600073|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aRAJAKUMARI, K|eResearcher |
| 110 | __ | |aDepartment of Engineering and Technology(Computer Science and Engineering)|bBharath University, Chennai|dChennai|ein |
| 245 | __ | |aA Novel Approach for Face Recognition Technique Using Various Pattern Models and Enhancing their Performance |
| 260 | __ | |aChennai|bBharath University, Chennai|c2019 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cDepartment of Engineering and Technology(Computer Science and Engineering), Bharath University, Chennai, Chennai|d2019|oD13CS005 |
| 520 | __ | |aRecently, there are various biometric systems available for face recognization. Even though Face recognition systems are already employed in various image organizing software webs, as applications in mobile devices and face biometric data in passport. This face recognization technique can be affected by features of the faces like shape, pose, reflectance and clarification that lead to even more complication. This research study subjects the strength and weakness of the face recognition techniques like as PCA (Principle Component Analysis), LDA (Linear Discriminant Analysis) and SVM (Support Vector Machine) model as a classifier and Euclidean as a distance measure, along with our proposed preprocessing technique. The reorganization rate obtained by the implementations is not adequate; therefore in this work we have implemented two different dimensionality reduction techniques PCA and LDA along with the SVM classifier to our proposed preprocessing method therefore to improve the reorganization rate. The expe |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Theory and Methods |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aNALINI, C|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/311574|yShodhganga |
| 905 | __ | |afromsg |
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