Raspberry pi-based finger vein recognition system using PCANet
| dc.contributor.author | Quek, Ee Wen | |
| dc.date.accessioned | 2021-04-15T06:11:38Z | |
| dc.date.available | 2021-04-15T06:11:38Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Finger Vein Recognition System (FVRS) is a biometric technology that identifies or verifies an individual identity based on unique vein patterns. Compared with other biometrics, it is more secure, anti-forgery and hygiene. Thus, it successfully utilized in many authentications nowadays. The original FVRS developed only provides verification instead of identification. For identification, the image processing involves process of image pre-processing, feature extraction and classification. The project utilised pre processing process such as edge detection, orientation correction and Region of Interest (ROI) extraction that have been developed previously. The main objective in this project is to implement a suitable feature extraction technique that can maximize the FVRS performance. A simple deep learning network, namely Principal Component Analysis Network (PCANet) is thus proposed. It composed of three basic data processing components, which are PCA filter, binary hashing and histograms. PCA is employed for learning multistage filter banks. Binary hashing and block histograms are the steps for indexing and pooling. A comparison between PCANet and PCA shows that PCANet is outperform under limited training samples, with an increase of 21.3% than that of PCA. Factors which impact PCANet are studied to identify the limitations of PCANet. For classification, k-Nearest Neighbours (kNN) with Euclidean distance algorithm is implemented. An enhancement version for kNN algorithm, k-General Nearest Neighbours (kGNN) have been proposed at initial stage. However, performance comparison between kNN, kGNN and SVM shows that kNN is more suitable for FVRS implementation. The last stage for this project is to combine previous work done into an embedded system which can be implemented in real finger vein authentication. The program is uploaded in the Raspberry Pi by using C++ language and OpenCV image processing library. The performance evaluation shows that the recognition rate of FVRS achieved 92.67% . Concluded that PCANet serve as a simple but highly competitive baseline in finger vein recognition. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/12868 | |
| dc.language.iso | en | en_US |
| dc.title | Raspberry pi-based finger vein recognition system using PCANet | en_US |
| dc.type | Other | en_US |
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