Neeta Nain

Bio: Neeta Nain is an academic researcher from Malaviya National Institute of Technology, Jaipur. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 13, co-authored 98 publications receiving 586 citations.

Longitudinal Study of Child Face Recognition

Abstract: We present a longitudinal study of face recognition performance on Children Longitudinal Face (CLF) dataset containing 3,682 face images of 919 subjects, in the age group [2,18] years. Each subject has at least four face images acquired over a time span of up to six years. Face comparison scores are obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTSA and FaceNet matchers. To improve the performance of the open-source FaceNet matcher for child face recognition, we were able to fine-tune it on an independent training set of 3,294 face images of 1,119 children in the age group [3,18] years. Multilevel statistical models are fit to genuine comparison scores from the CLF dataset to determine the decrease in face recognition accuracy over time. Additionally, we analyze both the verification and open-set identification accuracies in order to evaluate state-of-the-art face recognition technology for tracing and identifying children lost at a young age as victims of child trafficking or abduction.

A Hybrid Method of Feature Extraction for Facial Expression Recognition

Abstract: Facial Expression Recognition is necessary for designingany human-machine interface. The main issue of FacialExpression Recognition is to decide what features are requiredto represent a Facial Expression. In this paper, we proposethe hybrid technique for facial expression recognition. In thispaper we proposed a combined method of feature extractionusing Discrete Cosine Transform, Gabor Filter, Wavelet Transformand Gaussian distribution to improve the recognitionrate. Experimental are performed on seven expressions, (anger,disgust, fear, happiness, sadness, surprise, neutral ) of JAFFEdataset. The result of Proposed work is compared with result ofindividual Feature Extraction Techniques that show that FacialExpression Recognition Rate can be improved by combining bestfeatures of DCT, Gabor Filter, Wavelet Transform and GaussianDistribution.

A Hybrid Feature Extraction Algorithm for Devanagari Script

Abstract: The efficiency of any character recognition technique is directly dependent on the accuracy of the generated feature set that could uniquely represent a character and hence correctly recognize it. This article proposes a hybrid approach combining the structural features of the character and a mathematical model of curve fitting to simulate the best features of a character. As a preprocessing step, skeletonization of the character is performed using an iterative thinning algorithm based on Raster scan of the character image. Then, a combination of structural features of the character like number of endpoints, loops, and intersection points is calculated. Further, the thinned character image is statistically zoned into partitions, and a quadratic curve-fitting model is applied on each partition forming a feature vector of the coefficients of the optimally fitted curve. This vector is combined with the spatial distribution of the foreground pixels for each zone and hence script-independent feature representation. The approach has been evaluated experimentally on Devanagari scripts. The algorithm achieves an average recognition accuracy of 93.4p.

Crowd Monitoring and Classification: A Survey

Abstract: Crowd monitoring on public places is very demanding endeavor to accomplish. Huge population and assortment of human actions enforces the crowded scenes to be more continual. Enormous challenges occur into crowd management including proper crowd analysis, identification, monitoring and anomalous activity detection. Due to severe clutter and occlusions, conventional methods for dealing with crowd are not very effective. This paper highlights the various issues involved in analyzing crowd behavior and its dynamics along with classification of crowd analysis techniques. This review summarizes the shortcomings, strength and applicability of existing methods in different environmental scenarios. Furthermore, it overlays the path to device a proficient method of crowd monitoring and classification which can deal with most of the challenges related to this area.

Forecasting methods and applications.

Abstract: Wed, 05 Dec 2018 22:36:00 GMT forecasting methods and applications 3rd pdf PDF | On Jan 1, 1984, S ~G Makridakis and others published Forecasting: Methods and Applications Tue, 04 Dec 2018 23:06:00 GMT (PDF) Forecasting: Methods and Applications ResearchGate Forecasting: methods and applications. This book was published in 1998, and for nearly 20 years I maintained an associated website at this address. Fri, 30 Nov 2018 14:35:00 GMT Forecasting: methods and applications | Rob J Hyndman Prod 2100-2110 Forecasting Methods 2 1. Framework of planning decisions Let us first remember where the inventory control decisions may take place. Fri, 07 Dec 2018 14:13:00 GMT Forecasting Methods UCLouvain 2002 Forecasting: Methods and Applications Makridakis, ... this 3rd edition very wisely includes some more advanced forecasting methods such as dynamic regression, ... Sat, 01 Dec 2018 22:41:00 GMT 2002 Forecasting: Methods and Applications HEPHAESTUS Methods and Applications Third Edition Spyros Makridakis European Institute of Business ... major forecasting methods 516 The use of different forecasting Tue, 04 Dec 2018 22:37:00 GMT Methods and Applications Max Planck Society MATH6011: Forecasting “All models are wrong, ... S.C. and Hyndman, R.J. 1998, Forecasting: Methods and Applications 3rd Ed., New York: Wiley as text book. Wed, 21 Nov 2018 17:31:00 GMT MATH6011: Forecasting University of Southampton Save As PDF Ebook forecasting methods and applications ... FOUR LAMAS OF DOLPO AUTOBIOGRAPHIES OF FOUR TIBETAN LAMAS INTRODUCTION AND TRANSLATIONS VOL I 3RD [PDF] Tue, 04 Dec 2018 19:10:00 GMT forecasting methods and applications makridakis pdf ... forecasting methods and applications 3rd ed Download forecasting methods and applications 3rd ed or read online books in PDF, EPUB, Tuebl, and Mobi Format. Thu, 06 Dec 2018 07:26:00 GMT forecasting methods and applications 3rd ed | Download ... INDUSTRIAL STATISTICS AND OPERATIONAL MANAGEMENT 6 : ... Some applications of forecasting ... Qualitative techniques in forecasting Time series methods Mon, 19 Nov 2018 11:49:00 GMT INDUSTRIAL STATISTICS AND OPERATIONAL MANAGEMENT 6 ... 3 Hierarchical forecasting 9 3 Advanced methods 9. Forecasting: principles and practice 7 Assumptions • This is not an introduction to R. I assume you are broadly ... Thu, 06 Dec 2018 22:49:00 GMT Forecasting: Principles & Practice, Rob J Hyndman, 2014 forecasting methods and applications 3rd ed Download forecasting methods and applications 3rd ed or read online here in PDF or EPUB. Please click button to get ... Mon, 03 Dec 2018 08:27:00 GMT Forecasting Methods And Applications 3rd Ed | Download ... Forecasting methods can be classified as qualitative or quantitative. ... practical applications. 15-4 Chapter 15 Time Series Analysis and Forecasting Fri, 07 Dec 2018 12:33:00 GMT PDF Time Series Analysis and Forecasting Cengage FORECASTING METHODS AND APPLICATIONS 3RD EDITION PDF READ Forecasting Methods And Applications 3rd Edition pdf. Download Forecasting Methods And Applications 3rd ... Sun, 11 Nov 2018 17:14:00 GMT Free Forecasting Methods And Applications 3rd Edition PDF Forecasting Methods and Applications. 3rd ed. New York: John Wiley & Sons, 1998. Sat, 08 Dec 2018 09:40:00 GMT Forecasting Methods and Applications Book Harvard ... Preface In preparing the manuscript for the third edition of Forecasting: methods and applications, one of our primary goals has been to make the book as complete and ... Wed, 05 Dec 2018

Demographic Bias in Biometrics: A Survey on an Emerging Challenge

Abstract: Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g., access control) and noncooperative (e.g., surveillance and forensics) systems have benefited from biometrics. Such systems rely on the uniqueness of certain biological or behavioral characteristics of human beings, which enable for individuals to be reliably recognized using automated algorithms. Recently, however, there has been a wave of public and academic concerns regarding the existence of systemic bias in automated decision systems (including biometrics). Most prominently, face recognition algorithms have often been labeled as “racist” or “biased” by the media, nongovernmental organizations, and researchers alike. The main contributions of this article are: 1) an overview of the topic of algorithmic bias in the context of biometrics; 2) a comprehensive survey of the existing literature on biometric bias estimation and mitigation; 3) a discussion of the pertinent technical and social matters; and 4) an outline of the remaining challenges and future work items, both from technological and social points of view.

MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.

Abstract: This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.