Journal Information
Pattern Recognition Letters
Impact Factor:

Call For Papers
Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition.
Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition. Examples include:

• Statistical, structural, syntactic pattern recognition;
• Neural networks, machine learning, data mining;
• Discrete geometry, algebraic, graph-based techniques for pattern recognition;
• Signal analysis, image coding and processing, shape and texture analysis;
• Computer vision, robotics, remote sensing;
• Document processing, text and graphics recognition, digital libraries;
• Speech recognition, music analysis, multimedia systems;
• Natural language analysis, information retrieval;
• Biometrics, biomedical pattern analysis and information systems;
• Scientific, engineering, social and economical applications of pattern recognition;
• Special hardware architectures, software packages for pattern recognition.

We invite contributions as research reports or commentaries.

Research reports should be concise summaries of methodological inventions and findings, with strong potential of wide applications.
Alternatively, they can describe significant and novel applications of an established technique that are of high reference value to the same application area and other similar areas.

Commentaries can be lecture notes, subject reviews, reports on a conference, or debates on critical issues that are of wide interests.

To serve the interests of a diverse readership, the introduction should provide a concise summary of the background of the work in an accepted terminology in pattern recognition, state the unique contributions, and discuss broader impacts of the work outside the immediate subject area. All contributions are reviewed on the basis of scientific merits and breadth of potential interests.
Last updated by Dou Sun in 2017-08-07
Special Issues
Special Issue on Robustness, Security and Regulation Aspects in Current Biometric Systems (RSRA-BS)
Submission Date: 2017-09-30

Biometric systems consist in acquiring key physiological and/or behavioural features of humans, and use them for the automatic identification or verification of identity claims for physical protection. The urge for protection of sensitive infrastructure is calling for robust and secure biometric systems. In the first case, robustness is achieved by tolerating and dealing with the noise in the feature acquisition without affecting the correct outcome. This is achieved by investigating the number of false positive and false negative that noised feature acquisition causes and by proposing proper tolerance method to reduce such numbers. In the second case, a series of attacks can be directed towards a biometric system in order to bring it in error and alter the obtained result, by augmenting the number of false positive or the one of false negative. Moreover, a biometric system holds a number of data upon which the identification is performed, which may be considered sensitive and should keep private by the system. Currently, a series of proposal are being investigated in order to rise the offered level of robustness and security within such systems by using innovative pattern recognition systems and/or using multiple classifiers paving the way to multi-modal or multi-criteria biometrics. This is to respond to the more demanding market needs with respect to security and robustness by retaining high accuracy, scalability and usability. Last, recently a novel research topic is meeting greater attention and interest: when designing and deploying biometric systems it is important to consider the cultural, social and legal contexts of these systems. There is an increasing awareness of the social and legal aspects related to biometric systems, due to the fact that they are firmly tied to our physical bodies. There are considerable privacy concerns related to biometric systems: the legal status of biometric data, the storage of biometric data, compulsory and voluntary issues and the necessity of using biometric technology. Those concerns are calling out for legal regulations to discipline the use and design of biometric systems. The list of possible topics includes, but not limited to: - Robustness of the Biometric Systems and Its improvement - Regulatory and Legal Framework of Biometric Systems - Security and Trustworthiness of Biometric Systems - Privacy-enhancing biometrics - Biometric Systems for Security and Privacy - Pattern Recognition Innovations in Biometry - Novel biometric acquisition and storage - De-identification and Privacy in Soft Biometrics - Anti-Spoofing and Template Security
Last updated by Dou Sun in 2017-06-03
Special Issue on Graphonomics for e-citizens: e-health, e-society, e-education
Submission Date: 2017-10-31

Handwriting analysis and recognition has been widely studied for many years contributing to the development of a research field, which produced a large amount of both theoretical and experimental results. In this framework, the automatic processing of handwriting and drawing features, both on-line and off-line, in order to automatically classify specimens of handwriting, represents the core information processing technology behind many successful applications that are in daily use. Examples of the such applications can be found in human-machine interfaces, such as the electronic pen pad and automatic signature verification equipment, mail sorting, check reading and form processing, just to mention a few. The term graphonomics, coined in 1982, intends to capture the multidisciplinary and interdisciplinary nature of the entire research field. It denotes the scientific and technological effort involved in identifying relationships among the planning and generation of handwriting and drawing movements, the resulting spatial traces of writing and drawing instruments (either conventional or electronic), and the dynamic features of these traces. Even if many effective methods have been proposed in the literature and successfully applied in a number of real applications, these problems are still very far from being solved in the general case. The aim of this Special Issue is to bring together the works of many experts in this multidisciplinary subject that involves different competences and knowledge, which span from the study of the handwriting generation models to the development of machine learning techniques for handwriting recognition. The Special Issue should allow us to highlight the advances on these topics from a wide-angle perspective, as well as to stimulate new theoretical and applied research for better characterizing the state of the art in this subject. The special issue should follow the 18th Conference of the International Graphonomics Society (IGS2017) that will take place from 18 to 21 June 2017 in Gaeta, Italy, but submissions will be not restricted to IGS2017 contributors. TOPICS: - Handwriting recognition: Human reading; Pen computing; On-line and off-line recognition; - Cultural Heritage application: Historical document analysis and processing; Palaeography; Large digital archives. - Forensic applications: Handwriting features; Writer identification and verification; Signature verification; - Medical applications: Early detection and monitoring of neurological diseases implying handwriting disorders;
Last updated by Dou Sun in 2017-06-14
Special Issue on Learning Compact Representations for Scalable Visual Recognition and Retrieval
Submission Date: 2018-01-31

Motivations and Topics: With the explosive growth of visual data, traditional hand-crafted features or learning-based representations will induce inapplicable computational complexity and large memory costs, due to exhausting computations in large-scale and high-dimensional feature space. Therefore, these conventional methods are lack of scalability for large-scale visual applications, e.g. image/video recognition and retrieval. It is highly desirable to learn much more compact feature representations to reduce computational loads for massive visual data and make big data analysis more feasible. In recent years, compact feature learning algorithms have been intensively exploited and attracted broad interests in visual analysis. For instance, benefiting from the hashing technique, we can obtain compact binary representations, based on which efficient XOR computations in the Hamming space can operate in constant time. The above compact feature learning approaches have been proved to achieve promising performance in various large-scale visual applications, such as content-based image/video retrieval and recognition. In addition, these techniques will be essential for the applications on portable/wearable devices. The special issue will focus on the most recent progress on compact representation learning for a variety of large-scale visual data analysis, such as content-based image/video retrieval, image/video recognition, annotation and segmentation, object detection and recognition, visual processing and affective computing. This special issue will also target on related feature selection, subspace learning and deep learning techniques, which can well handle large-scale visual tasks. The primary objective of this special issue fosters focused attention on the latest research progress in this interesting area. The special issue seeks for original contributions of work, which addresses the challenges from the compact representation learning and the related efficient representation learning algorithms for large-scale visual data. The list of possible topics includes, but not limited to: Novel compact representation learning algorithms Large-scale visual (image, video) indexing algorithms Learning-based or data-dependent binary coding/hashing methods Novel vector quantization algorithms Visual recognition (e.g., detection, categorization, segmentation) with discriminative representation learning techniques Compact feature learning for object classification/detection/retrieval/tracking Novel applications of compact representation learning Deep learning techniques for compact representation learning Efficient feature extraction methods for visual data analysis Efficient learning algorithms for visual data representation Submission Guideline Authors should prepare their manuscript according to the Guide for Authors available from the online submission page of the Pattern Recognition Letters journal at All the papers will be peer-reviewed following the Pattern Recognition Letters reviewing procedures. When submitting their papers through the online system, Authors should select the acronym “SI:LCR4SVRR” to make it clear that they are submitting to this SI.
Last updated by Dou Sun in 2017-01-15
Special Issue on Multiple-task Learning for Big Data (ML4BD)
Submission Date: 2018-03-31

Big Data is a term that describes the large volume of data-both structured and unstructured. With the rapid development of new information technologies such as smart phone, mobile game platforms, smart home devices, smart health devices, and wearable computation devices, the amount of created and stored data on global level is almost inconceivable and it just keeps growing. These data is s- large and complex that traditional data processing applications are incapable of dealing with them. There are many challenges when addressing big data problem, such as data acquisition, data curation, data storage, data search, data transfer and sharing, data visualization, data query and retrieval, information security, and data analysis (e.g., prediction, user behavior analysis). Big data requires novel data processing techniques t- solve some of these challenges jointly, which is related t- Multiple-task Learning methodologies. Multiple-task Learning is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for each task-specific model, when compared t- training each of the models separately. Multiple-task Learning is an approach of inductive transfer that improves generalization by using domain information contained in the training signals of related tasks as inductive bias. It does this by learning multiple tasks in parallel using a shared representation, based on the assumption that all tasks can help each other in learning. MTL works because regularization induced by requiring an algorithm t- perform well on a related task can be superior t- regularization that prevents overfitting by penalizing all complexity uniformly. MTL may be particularly helpful if all related tasks share significant commonalities and are slightly under sampled. With big structured and unstructured data, different tasks on the same data or related data are suitable for MTL framework. Therefore, Multiple-task Learning for Big Data (MTL4BD) have broad applications in many fields, such as online recommendation system, smart home, smart health care, robotics, medical imaging, multimedia application, computer vision, human computer interaction and language processing, etc. We plan t- receive about 45 paper submissions from around 30 Universities and research institutions and we will accept about 15 papers plus a review paper totally.
Last updated by Dou Sun in 2017-04-13
Special Issue on Learning and Recognition for Assistive Computer Vision
Submission Date: 2018-06-30

Assistive Computer Vision refers to systems that support people with physical and mental problems to better perform daily tasks enhancing their quality of life. The advances in learning and recognizing patterns are allowing a point of view in the definition and development of more efficient and effective assistive frameworks. In the light of this, it is important to collect the most recent advancements in learning and recognition algorithms to be exploited in different applications to be employed to assist the modern society. The aim of the special issue is to gather papers in which machine learning and pattern recognition are the key core in the design of advanced assistive computer vision systems to help human in tasks such as: - Rehabilitation - Training - Mobility - Assessment and diagnosis of physical and cognitive diseases - Improving quality of Life - Remote Healthcare - Safe and security - Remote Surgery - Ambient Assisted Living - Augmented Perception, Attention and Memory We will invite authors to contribute with high quality paper that will stimulate the research community on building theory and applications of machine learning and pattern recognition to be used in real-life environments for assistive computer vision technologies.
Last updated by Dou Sun in 2017-04-13
Special Issue on Pattern Recognition Techniques for Non Verbal Human Behavior (NVHB)
Submission Date: 2018-07-31

Fundamental Cues for Non-Verbal behavioral are human communication and interaction. Despite Significant advances in recent years, state of the art human-machine systems still falls short in sensing, analyzing and fully understanding cues naturally expressed in everyday settings. Two of the most important non-verbal cues, evidenced by a large body of work in experimental psychology and behavioral sciences, are visual behavior and body language. Widely anticipated in HCI is that computing will move to the background, weaving itself into the fabric of our everyday living and projecting the human user into the foreground. To realize this goal, next-generation computing will need to develop human-centered user interfaces that respond readily to naturally occurring, multimodal, human communication. These interfaces will need the capacity to perceive, understand, and respond appropriately to human intentions and cognitive- emotional states as communicated by social and affective signals. Motivated by this visionof the future, automated analysis of nonverbal behavior has attracted increasing attention in diverse disciplines, including psychology, computer science, linguistics, and neuroscience. Promising approaches have been reported, especially in the areas of facial expression and multimodal communication. Yet, increasing evidence suggests that deliberate or posed behavior differs in appearance and timing from that which occurs in daily life. Approaches to automatic behavior analysis that have been trained on deliberate and typically exaggerated behaviors may fail to generalize to the complexity of expressive behavior found in real-world settings. This Virtual Special Issue (VSI) intends to bring together researchers and developers from academic fields and industries worldwide working in the broad areas of computer vision and promote community-wide discussion of ideas that will influence and foster continued research in this field for the betterment of human mankind. Papers submitted to this VSI and accepted for publication will be spread through several regular issues, since each accepted paper will be published as soon as possible without waiting until all submissions to the VSI are in final status. The accepted papers will also be gathered as part of a VSI that will be available exclusively online and will be gradually built up as the individual articles are published online. Recommended topics are given below: - Intelligent visual surveillance - Deep learning based Gait recognition - Machine Learning approaches - Semi supervised learning based behavior analysis - Deep learning for facial expression behavior - Real world application of behavior analysis - Time-critical techniques to understand gestural behavior - Sensor data interpretation for live behavior analysis
Last updated by Dou Sun in 2017-08-07
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