Offline signature recognition system using oriented FAST and rotated BRIEF

Abstract

Despite recent developments in offline signature recognition systems, there is however limited focus on the recognition problem facet of using an inadequate sample size for training that could deliver reliable and easy to use authentication systems. Signature recognition systems are one of the most popular biometric authentication systems. They are regarded as non-invasive, socially accepted, and adequately precise. Research on offline signature recognition systems still has not shown competent results when a limited number of signatures are used. This paper describes our proposed practical offline signature recognition system using the oriented FAST and rotated BRIEF (ORB) feature extraction algorithm. We focus on the practicality of the proposed system, which requires only the minimum number of signatures per user to achieve a high level of fidelity. We manifest the practicality of our approach with a signature database of 300 signatures from 100 different individuals, implying that only two signatures are needed per person to train the proposed system. Our proposed solution achieves a 91% recognition rate with a median matching time of only 7 ms.

A Real-Time Localization Algorithm for Unmanned Aerial Vehicle Based on Continuous Images Processing

“…In addition to the SIFT algorithm, there are also other image processing algorithms used for UAV localization. For example, the ORB (Oriented FAST and Rotated BRIEF) algorithm [14] is a fast feature detection and description algorithm that can provide faster speed than the SIFT algorithm, but may sacrifice some accuracy [15]. The SURF (Speeded Up Robust Features) algorithm [16] is also commonly used for UAV localization, which combines the advantages of the SIFT algorithm with the speed advantage of the FAST feature detection algorithm.…”

Pose estimation algorithm for mobile augmented reality based on inertial sensor fusion

“…The algorithm used in our project is ORB, as it provides better performance and minimal computational loads. ORB is an efficient feature detector and descriptor ideal for real-time situations where efficiency and speed are preferred [17], [18]. Also, it is free from any patent protection claims.…”

Pre-trained CNN Based Deep Features with Hand-Crafted Features and Patient Data for Skin Lesion Classification

“…Texture features: Texture features provide important information about the spatial disposition of the grey levels and the relationship with their neighbourhoods. For texture feature we have applied the following methods: SURF [11], SIFT [22], ORB [3], and LBP [32].…”