Double Layer PCA based Hyper Spectral Face Recognition using KNN Classifier

Dabhade, Siddharth B. and Bansod, Nagsen and Naveena, M. and Kavita, K. and Rode, Y. S. and Kazi, M. M. and Kale, K. V. (2017) Double Layer PCA based Hyper Spectral Face Recognition using KNN Classifier. In: International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), 08-09 September 2017, Mysore, India.

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Hyperspectral face recognition is a very challenging task as well as time-consuming process. Hyperspectral face images (HFI) are a very big size images as compare to normal RGB images. HFI is always more than 10 bands with spatial resolution and its size varies from camera to camera. To execute these large numbers of files, a big memory is required due to high dimensions. Principle Component Analysis is used with double layer feature extraction to reduce the dimensional size without losing the prominent features. Double layer PCA is applied on the Hong Kong Polytechnic University's Hyperspectral Face Database (PolyU-HSFD) and classify on the basis of k-nearest neighbor.

Item Type: Conference or Workshop Item (Paper)
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: Manjula P Library Assistant
Date Deposited: 07 Mar 2020 05:38
Last Modified: 16 Jun 2022 10:28

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