The Journal of Pattern Recognition Research (JPRR) provides an international forum for the electronic publication of high-quality research and industrial experience articles in all areas of pattern recognition, machine learning, and artificial intelligence. JPRR is committed to rigorous yet rapid reviewing. Final versions are published electronically
(ISSN 1558-884X) immediately upon acceptance.
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Article In Press: Violence Detection from ECG Signals: A Preliminary Study
JPRR Article #790
This research studied violence detection from less than 6-second ECG signals. Features were calculated based on the Bivariate Empirical Mode Decomposition (BEMD) and the Recurrence Quantification Analysis (RQA) applied to ECG signals from violence simulation in a primary school, involving 12 pupils from two grades. The feature sets were fed to a kNN classifier and tested using 10-fold cross validation and leave-one-subject-out (LOSO) validation in subject-dependent and subject-independent training models respectively. Features from BEMD outperformed the ones from RQA in both 10-fold cross validation, i.e. 88% vs. 73% (2nd grade pupils) and 87% vs. 81% (5th grade pupils), and LOSO validation, 77% vs. 75% (2nd grade pupils) and 80% vs. 76% (5th grade pupils), but have larger variation than the ones from RQA in both validations. Average performances for subject-specific system in 10-fold cross validation were 100% vs. 93% (2nd grade pupils) and 100% vs. 97% (5th grade pupils) for features from the BEMD and the RQA respectively. The results indicate that ECG signals as short as 6 seconds can be used successfully to detect violent events using subject-specific classifiers.
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JPRR selected for the Emerging Sources Citation Index (ESCI)
ESCI

We are pleased to announce that the Journal of Pattern Recognition Research (JPRR) has been selected for coverage in Thomson Reuters products and services. Beginning with content published in 2015, this publication will be indexed and abstracted in Emerging Sources Citation Index (ESCI).

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Vol 12, No 1 (2017)
Pattern Recognition Theory
Pattern Similarity Score Based on One Dimensional Time Series Analysis 1-6
A novel procedure to quantify similarity in different images based on time-series analysis is reported. Pattern recognition and matching operation includes parameters such as mean, correlation, mutual information etc.
JPRR Vol 12, No 1 (2017); doi:10.13176/11.744
Vol 11, No 1 (2016)
Applications
An Efficient Skew Estimation Technique for Scanned Documents: An Application of Piece-Wise Painting Algorithm 1-14
In this paper, an effcient skew estimation technique based on iterative employment of the Piece-wise Painting Algorithm (PPA) on document images is presented.
JPRR Vol 11, No 1 (2016); doi:10.13176/11.635
A Brief Review of Human Identification Using Eye Movement 15-25
The vulnerability of conventional biometrics to spoof has caused considerable concern especially in those fields that require high reliable user identification.
JPRR Vol 11, No 1 (2016); doi:10.13176/11.705
Application of Approximate Equality for Reduction of Feature Vector Dimension 26-40
Reduction of feature vector dimension is a problem of selecting the most informative features from an information system. Using rough set theory we can reduce the feature vector dimension when all the attribute values are crisp or discrete.
JPRR Vol 11, No 1 (2016); doi:10.13176/11.639
Surveying Biometric Authentication for Mobile Device Security 74-110
Mobile devices, such as smartphones and tablets, are frequently used for creation and transmission of private and sensitive messages and files.
JPRR Vol 11, No 1 (2016); doi:10.13176/11.764
Pattern Recognition Theory
Boosting Shape Classifiers Accuracy by Considering the Inverse Shape 41-54
Many techniques exist for describing shapes. These techniques almost exclusively consider the contour or the inside of the shape; the major problem for describing the outside of a shape, or inverse shape, being that it has an infinite extension.
JPRR Vol 11, No 1 (2016); doi:10.13176/11.727
Extending the K-Means Clustering Algorithm to Improve the Compactness of the Clusters 61-73
Clustering is a popular method essentially applied to data analysis, data mining, vector quantization and data compression. The most widely used clustering algorithm, which belongs to the group of partitioning algorithms, is the k-means.
JPRR Vol 11, No 1 (2016); doi:10.13176/11.745
Short Letters
Arabic OCR Using a Novel Hybrid Classification Scheme 55-60
JPRR Vol 11, No 1 (2016); doi:10.13176/11.711