Double Layer PCA based Hyper Spectral Face Recognition using KNN Classifier

被引：0

作者：

Dabhade, Siddharth B. ^{[1
]}

Bansod, Nagsen ^{[1
]}

Naveena, M.

Khobragade, Kavita ^{[2
]}

Rode, Y. S. ^{[3
]}

Kazi, M. M. ^{[4
]}

Kale, K., V ^{[1
]}

机构：

[1] Dr Babasaheb Ambedkar Marathwada Univ, Dept Comp Sci & IT, Aurangabad, Maharashtra, India

[2] Fergusson Coll Autonomous, Pune, Maharashtra, India

[3] Cidco, MIT Coll, Aurangabad, Maharashtra, India

[4] Dr GY Pathrikar Coll, Aurangabad, Maharashtra, India

来源：

2017 INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN COMPUTER, ELECTRICAL, ELECTRONICS AND COMMUNICATION (CTCEEC) | 2017年

关键词：

Face Recognition; Biometrics; PCA; KNN; Hyperspectral;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：289 / 293

页数：5

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