Information Processing and Pattern Recognition

Medical college libraries affiliated with Maharashtra University of Health Sciences (MUHS), Nashik, play a vital role in supporting education and research in the medical field. This study assesses user satisfaction with library resources and staff attitudes. Findings indicate high satisfaction with textbooks, journals, and audio-visual media, although some areas, like encyclopedias, show room for improvement. Additionally, most users are satisfied with staff attitudes, highlighting their professionalism. Recommendations include enhancing digital resources, improving staff training, and expanding interlibrary loan services. These measures can enhance user experiences, contributing to academic and professional growth in the medical community.

Audio Steganography is well known technique of hiding secrete text or audio information in host audio by modifying an audio signal in an imperceptible manner. Least Significant Bit (LSB) is the most commonly used technique for secure data transfer in audio steganography. e present paper focusses on n Th th bit replacement to hide a sequence of bytes of secret text and comparing the MSE and PSNR of SteganoAudio for all the bits.

This study sought to better understand OPAC utilization, user satisfaction, and native issues among South Indian pharmacy faculty members. 1553 users, including faculty members from a pharmacy college in South India, provided the primary data for this study. To gather data on the OPAC, a questionnaire, an online survey, and participant observation were employed as the data gathering methods. The resulting data was evaluated using fundamental statistical techniques to show the results in percentage and ranking formats. Data from primary sources showed that OPAC has to be developed considerably farther than the stages at which it was initially introduced to the pharmacy college in South India. Additionally, the system's facilities were inadequate, and the majority of users were ignorant of them. Along with that, it showed that when OPACs were still in their early phases, both users and staff members were unhappy. The mentioned issues can be resolved through appropriate and ongoing training for users and employees, technical advancements, and the availability of native language search options. This groundbreaking investigation of the OPACs' quality and issues is being conducted at a pharmacy college in South India. This study may encourage policy and decision makers to implement efficient OPACs at Pharmacy College in South India since certain libraries there are moving toward OPACs. This research proposes recommendations for user-friendly OPACs featured straightforward search tactics for Pharmacy College in South India, taking into consideration the approach of skilled users and their user behavior.

The Arabic Optical Characters Recognition (AOCR) is one of the challenging recognition tasks nowadays, as Arabic handwriting is cursive and contains many dots. Dots are a big challenge for Arabic recognizers, as writers sometimes connect them. Moreover, Dots are prone to be considered noise. This paper proposes a new divide and conquers based approach that tries to conquer dots problems. The novelty of the proposed approach is in its feature extraction method. The extracted features are used to train Artificial Neural Network (ANN) Feed-forward. The result is interesting and shows that this method should be further investigated.

The Arabic Optical Characters Recognition (AOCR) is one of the challenging recognition tasks nowadays, as Arabic handwriting is cursive and contains many dots. Dots are a big challenge for Arabic recognizers, as writers sometimes connect them. Moreover, Dots are prone to be considered noise. This paper proposes a new divide and conquers based approach that tries to conquer dots problems. The novelty of the proposed approach is in its feature extraction method. The extracted features are used to train Artificial Neural Network (ANN) Feed-forward. The result is interesting and shows that this method should be further investigated.

Image recognition and classification plays an important role in many applications, like driverless cars and online shopping. We present the classification of FashionMNIST (F-MNIST) dataset using two important classifiers SVM (Support Vector Machine) and CNN (Convolutional Neural Networks). In the first model two feature descriptors HOG (Histogram of Oriented Gradient) and Local Binary Pattern (LBP) with multiclass SVM. In this paper we explore the impact of various feature descriptors and classifiers on Fashion products classification tasks. We have used one of the most simple and effective single feature descriptor HOG. The multiclass SVM which is one of the best machine learning classifier algorithms is used in this method to train the images. In computer vision Convolutional Neural Networks (CNN or ConvNet) are the default deep learning model used for image classification problems. Selecting appropriate technique for feature extraction and choosing a best classifier algorithm rem...

Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT). Then, we trained the k-nearest neighbor’s algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance eval...

Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT). Then, we trained the k-nearest neighbor’s algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance eval...

Classifying and recognizing Arabic text is a hot topic for researchers due to its importance for both Muslim and Arab people. The importance of dealing with Arabic relies on the similarities among Arabic languages and other languages such as Farsi, Urdu, and Jawi. Moreover, The Holy Quran which is the holy book for all Muslims is written in Arabic. The Arabic language is written from right to left and it is used by more than 250 million all over the world [1][2]. Basically, there are different recognition systems for Arabic text based on their functions. Dealing with data acquisition, there are either online or off-line recognition systems. While dealing with the text type, there either printed or handwritten text recognition systems [3]. Using the online recognition systems, computers classify and recognize the text during its writing by a user which is done according to the movement of the pen. This means that online text recognition deals with handwritten text only. On the other ...

This paper presents a proposed system for recognizing the handwritten Arabic words. The proposed system recognized the Arabic word as one entity without using segmentation stage, which converted the word into parts. A proposed method for feature extraction stage used two groups of feature extraction techniques. First group combines two techniques and the second group used single technique. First group combines gradient (directional) feature method with the Run Length Count method and second group based on Discrete Cosine Translation technique. Classification stage is based on combined SVM with KNN classifiers. A standard data set which is AHDB database is used to simulate the proposed system. The recognition accuracy for the experimental results of the proposed system is 97.11 %.

Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT). Then, we trained the k-nearest neighbor's algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance evaluation results of the three k-NN generated models, the majority-voting algorithm is used to combine the prediction results. A high recognition rate of up to 99.88% is achieved, far exceeding the state-of-the-art results using the IFN/ENIT dataset. The obtained results highlight the reliability of the proposed system for the recognition of handwritten Arabic words.

An enhanced role and autonomy of perception are prominent in autism. Furthermore, savant abilities, absolute pitch, and synesthesia are all more commonly found in autistic individuals than in the typical population. The mechanism of veridical mapping has been proposed to account for how enhanced perception in autism leads to the high prevalence of these three phenomena and their structural similarity. Veridical mapping entails functional rededication of perceptual brain regions to higher order cognitive operations, allowing the enhanced detection and memorization of isomorphisms between perceptual and nonperceptual structures across multiple scales. In this paper, we present FC, an autistic individual who possesses several savant abilities in addition to both absolute pitch and synesthesia-like associations. The co-occurrence in FC of abilities, some of them rare, which share the same structure, as well as FC's own accounts of their development, together suggest the importance of veridical mapping in the atypical range and nature of abilities displayed by autistic people.

Liverdiseasesrepresentamajorhealthburdenworldwide.Machinelearning(ML)algorithmshave been extensively used to diagnose liver disease. This study accordingly aims to employ various individualandintegratedMLalgorithmsondistinctliverdiseasedatasetsforevaluatingthediagnostic performances,tointegratedimensionalityreductionmethodwiththeMLalgorithmsforanalyzing variationinresults,tofindthebestclassificationmodelandtoanalyzethemeritsanddemeritsof thesealgorithms.KNNandPCA-KNNemergedtobethetopindividualandintegratedmodels.The studyalsoconcludedthatonespecificalgorithmcan'tshowbestresultsforalltypesofdatasetsand integratedmodelsnotalwaysperformbetterthantheindividuals.Itisobservedthatnoalgorithmis perfectandperformanceofanalgorithmtotallydependsonthedatasettypeandstructure,itsnumber ofobservations,itsdimensionsandthedecisionboundary.

Automatic Recognition of Arabic Handwriting is a pervasive field that has many challenging complications to solve. Such complications include big databases and complex computing activities. The proposed approach is a multi-stage cascading recognition system bases on applying Random Forest classifier (RF) to construct a forest of decision trees. The constructed decision trees split big databases to multiple smaller data-mined sets based on the most discriminating computed geometric and regional features. Each data-mined set include similar database classes. RF match each test image with one of the data-mined sets. Afterwards, the matching classes are sorted relative to test image using Pyramid Histogram of Gradients and Kullback-Leibler based ranking algorithm. Finally, the classification process is applied on the highly ranked matching classes to assign a class membership to test image. Adjusting the classification process to only consider the highly ranked database classes reduced the computing classification and enhanced the overall performance. The proposed approach was tested on IFN-ENIT Arabic database and achieved satisfactory results and enhanced sensitivity of decision trees to reach 93.5% instead of 86.5% .

The Arabic Optical Characters Recognition (AOCR) is one of the challenging recognition tasks nowadays, as Arabic handwriting is cursive and contains many dots. Dots are a big challenge for Arabic recognizers, as writers sometimes connect them. Moreover, Dots are prone to be considered noise. This paper proposes a new divide and conquers based approach that tries to conquer dots problems. The novelty of the proposed approach is in its feature extraction method. The extracted features are used to train Artificial Neural Network (ANN) Feed-forward. The result is interesting and shows that this method should be further investigated.

In modern societies, handwritten identification is a great practical need and challenge for forensic sciences. Moreover, few research studies have focused on automatic offline handwritten document analysis. Also, there are very few standard datasets on handwritten document identification. In addition, handwritten documents lose their nature over time due to the spread and drying of ink. From this standpoint, the present study presents an offline writer identification system (in presence of the uncertainties such as different experimental conditions and environmental noises for more realistic assumptions) that is a critical need in forensic studies. For this purpose, a comprehensive right-to-left dataset is developed and devised by gathering data from 62 participants at different time intervals under different experimental conditions. This dataset is designed based on American Society for Testing and Materials (ASTM) standards. A Deep Convolutional Neural Network (DCNN) model based o...

Users are still waiting for accurate optical character recognition solutions for Arabic handwritten scripts. This research explores best sets of feature extraction techniques and studies the accuracy of well-known classifiers for Arabic letters. Depending on their position in the word, Arabic letters are drawn in four forms: Isolated, Initial, Medial, and Final. The principal component analysis technique is used to select best subset of features out of a large number of extracted features. We used parametric and non-parametric classifiers and found out that a subset of 25 features is needed to get 84% recognition accuracy using a linear discriminant classifier, and using more features does not substantially improve this accuracy. However, for features fewer than 25 features, a quadratic discriminant classifier is more accurate than the linear classifier. Classifiers that are parameterized for the individual four forms score better accuracy than classifiers that do not make use of this input information.

Application of ICT in library and information work has revolutionized the traditional concept of libraries from a ‘storehouse of books to an intellectual information centre’ connoting the concept of electronic or digital library. It has opened up a new chapter in library communication and facilitated global access to information crossing the geographical limitations. Using ICT, libraries are also playing a very important role in facilitating access to global information and knowledge resources.The paper explores the impact of ICT based resources and services available in three medical libraries on the undergraduate students. A total 50 questionnaires each were distributed to the medical students of the three colleges by convenient sampling to represent the overall population. The total number of responses received back from the users was 120 with a response rate of 80%. The findings revealed that Research purposes were found to be the most used option for respondents on the purpose of using ICT tools and services which amounts to 71.67 percent. Also use of e-resources witnessed that e-books were found to be the most used amongst various e-resources which accounts for 70 percent. The other used resources include e-journals (56.67%), e-magazines and e-newspapers (42.5% each), etc.

This research work is a report of the findings of a study of Information seeking pattern of Agricultural scientists working in Agricultural University of North India. A Structured questionnaire was used to know the information seeking behavior of agricultural scientists. The collected data was analyzed by using latest version of MS Excel. Simple Percentage system and scaling system like rating scales and rank order scales have been used as statistical procedures. The paper discusses the various types of information sources used by agricultural scientists, their preference and frequency. Their degree of dependence on formal and informal sources of information has been found out. It has been tried to find out the effectiveness of existing information systems and their adequacy regarding meeting information needs. The results show that Agriculture Scientists depend to a large extent on institutional libraries to meet their information requirements.

The study explores the extent of use of library facilities and services by the faculty members of selected arts and science government colleges affiliated to Bharathidasan University aiming at the information search pattern; purposes of seeking and collecting required information; preference of utilizing information resources and services; the users’ satisfaction on the services provided by the selected libraries; and the problems associated with information seeking. A total of 201 faculty members from five colleges were surveyed through questionnaire. It was found that majority of the respondents are found to be competent in searching and retrieving required information. However, there are a few problems identified in information search pattern.

The Arabic letters are used in many written languages. However, little work has been done to analyze and characterize handwritten Arabic letters comprehensively. Such characterization is important for the active research in the computer processing of Arabic written scripts. We extract carefully selected features from a large database of handwritten Arabic letters, from the letter's secondary components, main body, skeleton, and boundary. These features are studied and statistically analyzed to reach the targeted characterization. Observations about the important writing style variations are presented and statistically specified. The Arabic letters have multiple forms depending on the letter's position in the word. Comparisons among the four main letter forms (isolated, initial, medial, and final) are also presented.

Shape matching and point correspondence recovering play a fundamental role in applications like pattern and object recognition, shape classication, image alignment and regis- tration, visual information data mining, and many other computer vision related areas, which recently became active research disciplines due to the increasing needs in many practical elds. This article introduces the Contour-Point Signature (CPS), a novel technique

Abstract: Shape matching and point correspondence recovering play a fundamental role in applications like pattern and object recognition, shape classification, image alignment and registration, visual information data mining, and many other computer vision related areas, ...

Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, administration, teaching, and research in healthcare. It facilitates visual/automatic diagnosis and decision-making in real-time remote consultation/screening, store-and-forward tests, home care assistance and overall patient surveillance. Metrics help comparing visual data and improve diagnostic. Specially designed architectures can benefit from the application scenario. CBIR use calls for file storage standardization, querying procedures, efficient image transmission, realistic databases, global availability, access simplicity, and Internet-based structures. This chapter recommends important and complex aspects required to handle visual content in healthcare.

Subliminal information theory proposes that information is not only processed without awareness, but that it is also acted upon without awareness. Some research suggests such information is even prioritized over other forms of information processing. Where there remains some controversy over the extent or nature of the behavior that can be influenced by subliminal messages, there is little doubt that properly presented subliminal information is processed, retained, and acted upon. Contrary to popular opinion, the literature and evidence supporting subliminal information theory is robust. Indeed, meta-analysis clearly demonstrates very strong statistical support for this modality of care. There are many areas of research remaining to further enable taking advantage of subliminal information process- ing; however, because of the mis- and dis-information in the public domain, many scientists avoid this area, because of either a lack of knowledge or a fear of the kind of criticism that can influence careers.

Users are still waiting for accurate optical character recognition solutions for Arabic handwritten scripts. This research explores best sets of feature extraction techniques and studies the accuracy of well-known classifiers for Arabic letters. Depending on their position in the word, Arabic letters are drawn in four forms: Isolated, Initial, Medial, and Final. The principal component analysis technique is used to select best subset of features out of a large number of extracted features. We used parametric and non-parametric classifiers and found out that a subset of 25 features is needed to get 84% recognition accuracy using a linear discriminant classifier, and using more features does not substantially improve this accuracy. However, for features fewer than 25 features, a quadratic discriminant classifier is more accurate than the linear classifier. Classifiers that are parameterized for the individual four forms score better accuracy than classifiers that do not make use of this input information.

An enhanced role and autonomy of perception are prominent in autism. Furthermore, savant abilities, absolute pitch, and synesthesia are all more commonly found in autistic individuals than in the typical population. The mechanism of veridical mapping has been proposed to account for how enhanced perception in autism leads to the high prevalence of these three phenomena and their structural similarity. Veridical mapping entails functional rededication of perceptual brain regions to higher order cognitive operations, allowing the enhanced detection and memorization of isomorphisms between perceptual and nonperceptual structures across multiple scales. In this paper, we present FC, an autistic individual who possesses several savant abilities in addition to both absolute pitch and synesthesia-like associations. The co-occurrence in FC of abilities, some of them rare, which share the same structure, as well as FC's own accounts of their development, together suggest the importance of veridical mapping in the atypical range and nature of abilities displayed by autistic people.

The paper focuses on an overview of the different existing methods in content-based video retrieval. During the last decade there was a~rapid growth of video posted on the Internet. This imposes urgent demands on video retrieval. Video has a complex structure and can express the same idea in different ways. This makes the task of searching for video more complicated. Video titles and text descriptions cannot give the hole information about objects and events in the video. This creates a need for content-based video retrieval. There is a semantic gap between low-level video features, that can be extracted, and the users' perception. Complex content-based video retrieval can be regarded as the bridge between traditional retrieval and semantic-based video retrieval.

Keywords: frames; near-duplicates video; scenes; shots; video annotation; video classification; video mining; video reranking; video retrieval.

В статье предлагается обзор различных существующих методов ассоциативного поиска по видео. В течение прошлого десятилетия наблюдался стремительный рост количества видео размещаемых в Интернете, что создало острую необходимость в появлении поиска по видео. Видео имеет сложную структуру. Одна и та же информация может быть выражена различными способами. Это серьёзно усложняет задачу видео-поиска. Заголовки и описания видео не могут дать полного представления о самом видео, что влечет за собой необходимость использования ассоциативного поиска по видео. Существует семантический разрыв между низкоуровневыми характеристиками видео и восприятием пользователей. Комплексный ассоциативный видео-поиск может рассматриваться как связующее звено между обычным поиском и смысловым поиском по видео.

Ключевые слова: анализ видео; аннотирование видео; видео-поиск; кадры; классификация видео; нечеткие дубликаты видео; ранжирование видео; сцены; съёмки.

Никитин И. К. Элементы ассоциативного поиска по видео [Текст] / И. К. Никитин // Новое слово в науке: перспективы развития : материалы междунар. науч.–практ. конф. (Чебоксары, 10 сент. 2014 г.) / редкол.: О. Н. Широков [и др.]. – Чебоксары: ЦНС «Интерактив плюс», 2014. – С. 223–230. – ISBN 978-5-906626-37-0.

За последние несколько лет объем носителей мультимедийной информации вырос в несколько раз. В сложившейся ситуации необходим простой и гибкий поиск неструктурированным мультимедийных данных. В статье предлагается обзор  существующих методов ассоциативного поиска по видео.

The objective of this work is to recognize all the frontal faces of a character in the closed world of a movie or situation comedy, given a small number of query faces. This is challenging because faces in a feature-length film are relatively uncontrolled with a wide variability of scale, pose, illumination, and expressions, and also may be partially occluded. We develop a recognition method based on a cascade of processing steps that normalize for the effects of the changing imaging environment. In particular there are three areas of novelty: (i) we suppress the background surrounding the face, enabling the maximum area of the face to be retained for recognition rather than a subset; (ii) we include a pose refinement step to optimize the registration between the test image and face exemplar; and (iii) we use robust distance to a sub-space to allow for partial occlusion and expression change. The method is applied and evaluated on several feature length films. It is demonstrated that high recall rates (over 92%) can be achieved whilst maintaining good precision (over 93%).

In this paper we consider face recognition from sets of face images and, in particular, recognition invariance to illumination. The main contribution is an algorithm based on the novel concept of Maximally Probable Mutual Modes (MMPM). Specifically: (i) we discuss and derive a local manifold illumination invariant and (ii) show how the invariant naturally leads to a formulation of "common modes" of two face appearance distributions. Recognition is then performed by finding the most probable mode, which is shown to be an eigenvalue problem. The effectiveness of the proposed method is demonstrated empirically on a challenging database containing the total of 700 video sequences of 100 individuals.

This paper addresses the problem of tracking moving objects of variable appearance in challenging scenes rich with features and texture. Reliable tracking is of pivotal importance in surveillance applications. It is made particularly difficult by the nature of objects encountered in such scenes: these too change in appearance and scale, and are often articulated (e.g. humans). We propose a method which uses fast motion detection and segmentation as a constraint for both building appearance models and their robust propagation (matching) in time. The appearance model is based on sets of local appearances automatically clustered using spatio-kinetic similarity, and is updated with each new appearance seen. This integration of all seen appearances of a tracked object makes it extremely resilient to errors caused by occlusion and the lack of permanence of due to low data quality, appearance change or background clutter. These theoretical strengths of our algorithm are empirically demonstrated on two hour long video footage of a busy city marketplace.

Linear subspace representations of appearance variation are pervasive in computer vision. In this paper we address the problem of robustly matching them (computing the similarity between them) when they correspond to sets of images of different (possibly greatly so) scales. We show that the naïve solution of projecting the low-scale subspace into the high-scale image space is inadequate, especially at large scale discrepancies. A successful approach is proposed instead. It consists of (i) an interpolated projection of the low-scale subspace into the high-scale space, which is followed by (ii) a rotation of this initial estimate within the bounds of the imposed “downsampling constraint”. The optimal rotation is found in the closed-form which best aligns the high-scale reconstruction of the low-scale subspace with the reference it is compared to. The proposed method is evaluated on the problem of matching sets of face appearances under varying illumination. In comparison to the naïve matching, our algorithm is shown to greatly increase the separation of between-class and within-class similarities, as well as produce far more meaningful modes of common appearance on which the match score is based.

In this paper we are interested in analyzing behaviour in crowded public places at the level of holistic motion. Our aim is to learn, without user input, strong scene priors or labelled data, the scope of "normal behaviour" for a particular scene and thus alert to novelty in unseen footage. The first contribution is a low-level motion model based on what we term tracklet primitives, which are scene-specific elementary motions. We propose a clustering-based algorithm for tracklet estimation from local approximations to tracks of appearance features. This is followed by two methods for motion novelty inference from tracklet primitives: (a) an approach based on a non-hierarchial ensemble of Markov chains as a means of capturing behavioural characteristics at different scales, and (b) a more flexible alternative which exhibits a higher generalizing power by accounting for constraints introduced by intentionality and goal-oriented planning of human motion in a particular scene. Evaluated on a 2h long video of a busy city marketplace, both algorithms are shown to be successful at inferring unusual behaviour, the latter model achieving better performance for novelties at a larger spatial scale.

Illumination invariance remains one of the most researched, yet the most challenging aspect of automatic face recognition. In this paper the discriminative power of colour-based invariants is investigated in the presence of large illumination changes between training and query data, when appearance changes due to cast shadows and non-Lambertian effects are significant. Specifically, there are three main contributions: (i) a general photometric model of the camera is described and it is shown how its parameters can be estimated from realistic video input of pseudo-random head motion, (ii) several novel colour-based face invariants are derived for different special instances of the camera model, and (iii) the performance of the largest number of colour-based representations in the literature is evaluated and analysed on a database of 700 video sequences. The reported results suggest that: (i) colour invariants do have a substantial discriminative power which may increase the robustness and accuracy of recognition from low resolution images in extreme illuminations, and (ii) that the non-linearities of the general photometric camera model have a significant effect on recognition performance. This highlights the limitations of previous work and emphasizes the need to assess face recognition performance using training and query data which had been captured by different acquisition equipment.

Our aim in this paper is to robustly match frontal faces in the presence of extreme illumination changes, using only a single training image per person and a single probe image. In the illumination conditions we consider, which include those with the dominant light source placed behind and to the side of the user, directly above and pointing downwards or indeed below and pointing upwards, this is a most challenging problem. The presence of sharp cast shadows, large poorly illuminated regions of the face, quantum and quantization noise and other nuisance effects, makes it difficult to extract a sufficiently discriminative yet robust representation. We introduce a representation which is based on image gradient directions near robust edges which correspond to characteristic facial features. Robust edges are extracted using a cascade of processing steps, each of which seeks to harness further discriminative information or normalize for a particular source of extra-personal appearance variability. The proposed representation was evaluated on the extremely difficult YaleB data set. Unlike most of the previous work we include all available illuminations, perform training using a single image per person and match these also to a single probe image. In this challenging evaluation setup, the proposed gradient edge map achieved 0.8% error rate, demonstrating a nearly perfect receiver-operator characteristic curve behaviour. This is by far the best performance achieved in this setup reported in the literature, the best performing methods previously proposed attaining error rates of approximately 6-7%.

In this paper we address the problem of matching sets of vectors embedded in the same input space. We propose an approach which is motivated by canonical correlation analysis (CCA), a statistical technique which has proven successful in a wide variety of pattern recognition problems. Like CCA when applied to the matching of sets, our extended canonical correlation analysis (E-CCA) aims to extract the most similar modes of variability within two sets. Our first major contribution is the formulation of a principled framework for robust inference of such modes from data in the presence of uncertainty associated with noise and sampling randomness. E-CCA retains the efficiency and closed form computability of CCA, but unlike it, does not possess free parameters which cannot be inferred directly from data (inherent data dimensionality, and the number of canonical correlations used for set similarity computation). Our second major contribution is to show that in contrast to CCA, E-CCA is readily adapted to match sets in a discriminative learning scheme which we call discriminative extended canonical correlation analysis (DE-CCA). Theoretical contributions of this paper are followed by an empirical evaluation of its premises on the task of face recognition from sets of rasterized appearance images. The results demonstrate that our approach, E-CCA, already outperforms both CCA and its quasi-discriminative counterpart constrained CCA (C-CCA), for all values of their free parameters. An even greater improvement is achieved with the discriminative variant, DE-CCA.

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their own, most notably sensitivity to factors which affect facial heat emission patterns, e.g.\ emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, to normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first publication which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances detail in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the exact pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieved 100% identification rate, significantly outperforming previously described methods.

Some low-IQ savants can give the day of the week corresponding to decades’ worth of dates, but cannot state the number of dates in a week or solve a simple addition or subtraction problem. This divergence seems paradoxical because we divide the world into  conscious, complex, “high-level” cognitive processes and unconscious, simple, “low-level” perceptual-motor ones.  Yet savant abilities fit neither category: savants’ abilities, though not fully conscious, involve more than rote memory.  Indeed, savants eventually come to resemble neurotypical experts, and may even become creative.  Unsupervised neural network models may illustrate how savants learn: through unconscious, yet very high-level, pattern recognition.  Savants seek out domains that have meaningful structural regularities, e.g. calendars, math, or music, and spend a great deal of time and attention on such domains.  They may implicitly learn these regularities through a sort of pattern recognition.  This theory implies that pattern recognition is more than just a low-level perceptual process, and more importantly, that complex cognitive processes can be unconscious.

Shape matching and point correspondence recovering play a fundamental role in applications like pattern and object recognition, shape classification, image alignment and registration, visual information data mining, and many other computer vision related areas, which recently became active research disciplines due to the increasing needs in many practical fields. This article introduces the Contour-Point Signature (CPS), a novel technique that produces features that are invariant to translation, scaling, and rotation. The approach make use of CPS to find the correspondences of points extracted from the contours of two similar shapes and to establish a relation to map arbitrary points from one shape to another.