Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: FINGERPRINT CLASSIFICATION BASED ON NOVEL DIRECTIONAL FEATURES USING NEURAL NETWORK
Fingerprint Classification by Directional Image Partitioning
A survey of fingerprint classification Part I: Taxonomies on feature extraction methods and learning models
Adaptive flow orientation-based feature extraction in fingerprint images
Fingerprint Classification using Fast Fourier Transform and Nonlinear Discriminant Analysis
Local line directional pattern for palmprint recognition
Classification of Fingerprint Images
Fingerprint Classification
K-Nearest Neighbor Classification Approach for Face and Fingerprint at Feature Level Fusion
Gender classification with KNN by extraction of Haar wavelet features from canny shape fingerprints
Fingerprint pattern classification
Improvement of Fingerprint Retrieval by a Statistical Classifier
FingerNet: An unified deep network for fingerprint minutiae extraction
A model-based method for the computation of fingerprints' orientation field
Deep belief network based statistical feature learning for fingerprint liveness detection
Improving Fingerprint Orientation Extraction
Perfect Fingerprint Orientation Fields by Locally Adaptive Global Models
Robust classification of different fingerprint copies with deep neural networks for database penetration rate reduction
- 8 ) fingerprint recognition by classification using neural network and matching using minutia ( fingerprint recognition by nnmm method ) .
A survey of fingerprint classification Part II: Experimental analysis and ensemble proposal
Indexing Fingerprint Databases Based on Multiple Features
Dropout: a simple way to prevent neural networks from overfitting
An anatomical study of a new portal for ankle arthroscopy
Examining Adoption of e-Procurement in Public Sector Using the Perceived Characteristics of Innovating: Indonesian Perspective
Computer animation data management : Review of evolution phases and emerging issues
Predicting future hourly residential electrical consumption: A machine learning case study