294 research outputs found
A Novel Dataset for Non-Destructive Inspection of Handwritten Documents
Forensic handwriting examination is a branch of Forensic Science that aims to
examine handwritten documents in order to properly define or hypothesize the
manuscript's author. These analysis involves comparing two or more (digitized)
documents through a comprehensive comparison of intrinsic local and global
features. If a correlation exists and specific best practices are satisfied,
then it will be possible to affirm that the documents under analysis were
written by the same individual. The need to create sophisticated tools capable
of extracting and comparing significant features has led to the development of
cutting-edge software with almost entirely automated processes, improving the
forensic examination of handwriting and achieving increasingly objective
evaluations. This is made possible by algorithmic solutions based on purely
mathematical concepts. Machine Learning and Deep Learning models trained with
specific datasets could turn out to be the key elements to best solve the task
at hand. In this paper, we proposed a new and challenging dataset consisting of
two subsets: the first consists of 21 documents written either by the classic
``pen and paper" approach (and later digitized) and directly acquired on common
devices such as tablets; the second consists of 362 handwritten manuscripts by
124 different people, acquired following a specific pipeline. Our study
pioneered a comparison between traditionally handwritten documents and those
produced with digital tools (e.g., tablets). Preliminary results on the
proposed datasets show that 90% classification accuracy can be achieved on the
first subset (documents written on both paper and pen and later digitized and
on tablets) and 96% on the second portion of the data. The datasets are
available at
https://iplab.dmi.unict.it/mfs/forensic-handwriting-analysis/novel-dataset-2023/.Comment: arXiv admin note: text overlap with arXiv:2310.1121
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Pattern mining approaches used in sensor-based biometric recognition: a review
Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems
Biometric Systems
Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications
Multi-feature approach for writer-independent offline signature verification
Some of the fundamental problems facing handwritten signature verification are the large number of users, the large number of features, the limited number of reference signatures for training, the high intra-personal variability of the signatures and the unavailability of forgeries as counterexamples. This research first presents a survey of offline signature verification techniques, focusing on the feature extraction and verification strategies. The goal is to present the most important advances, as well as the current challenges in this field. Of particular interest are the techniques that allow for designing a signature verification system based on a limited amount of data. Next is presented a novel offline signature verification system based on multiple feature extraction techniques, dichotomy transformation and boosting feature selection. Using multiple feature extraction techniques increases the diversity of information extracted from the signature, thereby producing features that mitigate intra-personal variability, while dichotomy transformation ensures writer-independent classification, thus relieving the verification system from the burden of a large number of users. Finally, using boosting feature selection allows for a low cost writer-independent verification system that selects features while learning. As such, the proposed system provides a practical framework to explore and learn from problems with numerous potential features. Comparison of simulation results from systems found in literature confirms the viability of the proposed system, even when only a single reference signature is available. The proposed system provides an efficient solution to a wide range problems (eg. biometric authentication) with limited training samples, new training samples emerging during operations, numerous classes, and few or no counterexamples
Deep Learning Detected Nutrient Deficiency in Chili Plant
Chili is a staple commodity that also affects the Indonesian economy due to high market demand.
Proven in June 2019, chili is a contributor to Indonesia's inflation of 0.20% from 0.55%. One
factor is crop failure due to malnutrition. In this study, the aim is to explore Deep Learning
Technology in agriculture to help farmers be able to diagnose their plants, so that their plants
are not malnourished. Using the RCNN algorithm as the architecture of this system. Use 270
datasets in 4 categories. The dataset used is primary data with chili samples in Boyolali Regency,
Indonesia. The chili we use are curly chili. The results of this study are computers that can
recognize nutrient deficiencies in chili plants based on image input received with the greatest
testing accuracy of 82.61% and has the best mAP value of 15.57%
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