An Intelligent System for Automatic Fingerprint Identification using Feature Fusion by Gabor Filter and Deep Learning

This paper introduces an intelligent computational approach to automatically authenticate fingerprint for personal identification and verification. The feature vector is formed using combined features obtained from Gabor filtering technique and deep learning technique such as Convolutional Neural Network (CNN). Principle Component Analysis (PCA) has been performed on the feature vectors to reduce the overfitting problems in order to make the classification results more accurate and reliable. A multiclass classifier has been trained using the extracted features. Experiments performed using standard public databases demonstrated that the proposed approach showed better performance with regard to accuracy (99.87%) compared to the more recent classification techniques such as Support Vector Machine (97.86%) or Random Forest (95.47%). However, the proposed method also showed higher accuracy compared to other validation approaches such as K-fold (98.89%) and generalization (97.75%). Furthermore, these results were supported by confusion matrix results where only 10 failures were found when tested with 5000 images

Implementasi point inclusion in polygon test untuk pendataan kehadiran pegawai berbasis Biometrik di perangkat Mobile

Sistem kehadiran menggunakan fingerprint sudah banyak digunakan pada perusahaan. Tetapi sistem ini memiliki beberapa kekurangan, salah satunya adalah mobilitas. Banyak penelitian yang dilakukan terkait dengan peningkatan sistem kehadiran, salah satunya memanfaatkan teknologi mobile. Penelitian ini mengembangkan aplikasi pendataan kehadiran pegawai berbasis biometrik di perangkat mobile. Sistem yang dikembangkan menggunakan metode Point Inclusion in Polygon Test untuk membatasi penggunaan aplikasi hanya di dalam wilayah poligon yang sudah ditentukan. Aplikasi yang dihasilkan dapat dijalankan pada sistem operasi Android dan IOS yang memiliki sensor fingerprint. Hasil dari percobaan yang dilakukan untuk menganalisa metode yang diimplementasikan ke dalam aplikasi menunjukan keberhasilan sebesar 87%, tergantung dengan sensor GPS yang digunakan pada perangkat mobile. Percobaan kedua dilakukan untuk melihat network latency menghasilkan kesimpulan bahwa jaringan 4G pada Android 8 adalah solusi terbaik ketika menggunakan aplikasi ini

A Large-Scale Study of Fingerprint Matching Systems for Sensor Interoperability Problem

The fingerprint is a commonly used biometric modality that is widely employed for authentication by law enforcement agencies and commercial applications. The designs of existing fingerprint matching methods are based on the hypothesis that the same sensor is used to capture fingerprints during enrollment and verification. Advances in fingerprint sensor technology have raised the question about the usability of current methods when different sensors are employed for enrollment and verification; this is a fingerprint sensor interoperability problem. To provide insight into this problem and assess the status of state-of-the-art matching methods to tackle this problem, we first analyze the characteristics of fingerprints captured with different sensors, which makes cross-sensor matching a challenging problem. We demonstrate the importance of fingerprint enhancement methods for cross-sensor matching. Finally, we conduct a comparative study of state-of-the-art fingerprint recognition methods and provide insight into their abilities to address this problem. We performed experiments using a public database (FingerPass) that contains nine datasets captured with different sensors. We analyzed the effects of different sensors and found that cross-sensor matching performance deteriorates when different sensors are used for enrollment and verification. In view of our analysis, we propose future research directions for this problem