Writer identification using curvature-free features

Feature engineering takes a very important role in writer identification which has been widely studied in the literature. Previous works have shown that the joint feature distribution of two properties can improve the performance. The joint feature distribution makes feature relationships explicit instead of roping that a trained classifier picks up a non-linear relation present in the data. In this paper, we propose two novel and curvature-free features: run-lengths of local binary pattern (LBPruns) and cloud of line distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized and gray scale images. The COLD feature is the joint distribution of the relation between orientation and length of line segments obtained from writing contours in handwritten documents. Our proposed LBPruns and COLD are textural-based curvature-free features and capture the line information of handwritten texts instead of the curvature information. The combination of the LBPruns and COLD features provides a significant improvement on the CERUG data set, handwritten documents on which contain a large number of irregular-curvature strokes. The results of proposed features evaluated on other two widely used data sets (Firemaker and IAM) demonstrate promising results

Multi-Order Statistical Descriptors for Real-Time Face Recognition and Object Classification

We propose novel multi-order statistical descriptors which can be used for high speed object classification or face recognition from videos or image sets. We represent each gallery set with a global second-order statistic which captures correlated global variations in all feature directions as well as the common set structure. A lightweight descriptor is then constructed by efficiently compacting the second-order statistic using Cholesky decomposition. We then enrich the descriptor with the first-order statistic of the gallery set to further enhance the representation power. By projecting the descriptor into a low-dimensional discriminant subspace, we obtain further dimensionality reduction, while the discrimination power of the proposed representation is still preserved. Therefore, our method represents a complex image set by a single descriptor having significantly reduced dimensionality. We apply the proposed algorithm on image set and video-based face and periocular biometric identification, object category recognition, and hand gesture recognition. Experiments on six benchmark data sets validate that the proposed method achieves significantly better classification accuracy with lower computational complexity than the existing techniques. The proposed compact representations can be used for real-time object classification and face recognition in videos. 2013 IEEE.This work was supported by NPRP through the Qatar National Research Fund (a member of Qatar Foundation) under Grant 7-1711-1-312.Scopu

Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers

We present a new approach for online handwritten signature classification and verification based on descriptors stemming from Information Theory. The proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher Information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results produced surpass state-of-the-art techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.Comment: Submitted to PLOS On

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Invariant encoding schemes for visual recognition

Many encoding schemes, such as the Scale Invariant Feature Transform (SIFT) and Histograms of Oriented Gradients (HOG), make use of templates of histograms to enable a loose encoding of the spatial position of basic features such as oriented gradients. Whilst such schemes have been successfully applied, the use of a template may limit the potential as it forces the histograms to conform to a rigid spatial arrangement. In this work we look at developing novel schemes making use of histograms, without the need for a template, which offer good levels of performance in visual recognition tasks. To do this, we look at the way the basic feature type changes across scale at individual locations. This gives rise to the notion of column features, which capture this change across scale by concatenating feature types at a given scale separation. As well as applying this idea to oriented gradients, we make wide use of Basic Image Features (BIFs) and oriented Basic Image Features (oBIFs) which encode local symmetry information. This resulted in a range of encoding schemes. We then tested these schemes on problems of current interest in three application areas. First, the recognition of characters taken from natural images, where our system outperformed existing methods. For the second area we selected a texture problem, involving the discrimination of quartz grains using surface texture, where the system achieved near perfect performance on the first task, and a level of performance comparable to an expert human on the second. In the third area, writer identification, the system achieved a perfect score and outperformed other methods when tested using the Arabic handwriting dataset as part of the ICDAR 2011 Competition

Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddressed research field, because of several factors. Complications arise due to the cursive nature of the Arabic writing, character similarities, unlimited vocabulary, use of multi-size and mixed-fonts, etc. To handle these challenges, an automatic Arabic text recognition requires building a robust system by computing discriminative features and applying a rigorous classifier together to achieve an improved performance. In this work, we introduce a new deep learning based system that recognizes Arabic text contained in images. We propose a novel hybrid network, combining a Bag-of-Feature (BoF) framework for feature extraction based on a deep Sparse Auto-Encoder (SAE), and Hidden Markov Models (HMMs), for sequence recognition. Our proposed system, termed BoF-deep SAE-HMM, is tested on four datasets, namely the printed Arabic line images Printed KHATT (P-KHATT), the benchmark printed word images Arabic Printed Text Image (APTI), the benchmark handwritten Arabic word images IFN/ENIT, and the benchmark handwritten digits images Modified National Institute of Standards and Technology (MNIST)

Spectrogram classification using dissimilarity space

In this work, we combine a Siamese neural network and different clustering techniques to generate a dissimilarity space that is then used to train an SVM for automated animal audio classification. The animal audio datasets used are (i) birds and (ii) cat sounds, which are freely available. We exploit different clustering methods to reduce the spectrograms in the dataset to a number of centroids that are used to generate the dissimilarity space through the Siamese network. Once computed, we use the dissimilarity space to generate a vector space representation of each pattern, which is then fed into an support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Our study shows that the proposed approach based on dissimilarity space performs well on both classification problems without ad-hoc optimization of the clustering methods. Moreover, results show that the fusion of CNN-based approaches applied to the animal audio classification problem works better than the stand-alone CNNs

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p