Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
Browse
2 results
Search Results
Now showing 1 - 2 of 2
Master Thesis Synthetic Generation of Fingerprints(Izmir Institute of Technology, 2020) İrtem, Emre; Erdoğmuş, NesliFingerprints are unique to each person and they have been widely used and accepted for identification purposes by the society. Fingerprints can be captured by using ink and paper to get a print and then digitizing it or more recently by using specialized sensors. But in both cases, trained specialist supervision is mostly needed. Moreover, since fingerprints are personal information, they are protected by the laws on personal data protection. Therefore, collection/sharing of real fingerprints is difficult and illegal without the consent of their owner. On the otherhand, deep learning systems that are proven to be very successfull in many machine learning task, usually depend on very large training sets to achive high accuracies. In this study, to overcome the data hunger problem for training deep neural networks, synthetic fingerprints are generated by using model-based methods. For this purpose, firstly master fingerprint images are generated and next many impressions are derived from them by applying real-world degradations. The realism and the usability of synthetic fingerprints are tried and validated using a fingerprint classification system. For which, a deep neural networks are trained with and without the synthetically generated data. As a result of the experiments, it is shown that the generated fingerprint images are realistic enough to positively effect the classification results and that the usage of the synthetically generated fingerprints in training deep systems are promising.Master Thesis Deep Learning in Fingerprint Analysis(Izmir Institute of Technology, 2020) İrtem, Pelin; Erdoğmuş, Nesli; Erdoğmuş, NesliFingerprints are one of the most widely used personal identification traits. They play a crucial role in forensics because they are considered to be unique to each person. For many years, the identification of individuals had been carried out by human operators. However, with technological developments, automated fingerprint recognition systems have arisen, and the growth in the population has increased the importance of their robustness. On the other hand, deep learning has led to many impressive developments in the area of computer vision. Fingerprint analysis is indeed in the scope of image processing and computer vision; however, the usage of deep learning in fingerprint analysis is rather limited. This study focuses on using deep learning techniques on two different stages of the automated fingerprint recognition pipeline: Fingerprint classification and fingerprint minutiae extraction. Deep learning systems are developed for those two selected stages and analysed with respect to several aspects such as dataset size and different network architectures.