Gender Recognition Based on Facial Image Extraction PDF Download

Author: Nurul Zarina Md Isa
Publisher:
ISBN:
Category : Biometric identification
Languages : en
Pages : 66

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Book Description
In principle, to combine face detection and gender classification methods may seem simple. However, this process is more complex than it appears because requires many aspects for consideration. The gender classification has attracted much attention in psychological literature, relatively few machine vision methods have been proposed. However it has been extensively studied in the context of surveillance applications and biometrics. This project is mainly concern with offline gender classification using purely image processing technique which using a database that was included in the system. The way of doing this is by extracting the differences between male and female facial features. Obviously the classification base on a single feature is not adequate since humans share many facial properties even within different gender group. So multilayer processing is needed. This project is working as expected based on the scope and objective of project. Although not many varieties of facial images have been considered like colored hair the basic techniques should be just the same. For the system classification, Template Matching Technique is used to match image with the database image. The system attempts are made to capture the most appropriate representation of face images as a whole and exploit the statistical regularities of pixel intensity variations. When attempting recognition, the unclassified image is compared with all the database images, returning a vector of matching score. The unknown person is then classified as the one giving the highest cumulative score. This project will be build using the MATLAB software. Overall, the project can be used and developed for various purposes, particularly to expedite the process of searching the database. The refinement of this project in other hand can lead to more accurate and reliable result by considering other facial properties like eyes, nose and eyebrows.

Author: Massimo Tistarelli
Publisher: Springer Science & Business Media
ISBN: 3642017924
Category : Business & Economics
Languages : en
Pages : 1323

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Book Description
This book constitutes the refereed proceedings of the Third International Conference on Biometrics, ICB 2009, held in Alghero, Italy, June 2-5, 2009. The 36 revised full papers and 93 revised poster papers presented were carefully reviewed and selected from 250 submissions. Biometric criteria covered by the papers are assigned to face, speech, fingerprint and palmprint, multibiometrics and security, gait, iris, and other biometrics. In addition there are 4 papers on challenges and competitions that currently are under way, thus presenting an overview on the evaluation of biometrics.

Author: Emon Kumar Dey
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659412097
Category :
Languages : en
Pages : 80

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Book Description
Computer vision-based gender detection from facial image is a challenging and important task for computer vision-based researchers. The automatic gender detection from face image has potential applications in visual surveillance and human-computer interaction sys- tems (HCI). Human faces provide important visual information for gender perception. The system described in this book can automatically detect face from input images and the detected facial area is taken as region of interest (ROI). Some techniques and algorithm of Image Processing is applied on that ROI which identifies the gender of the face image.The experimental reseult described on chapter 4 in this book finds the accuracy of the system is more than 80%.

Author: Stefan Duffner
Publisher: GRIN Verlag
ISBN: 364039769X
Category : Computers
Languages : en
Pages : 197

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Book Description
Doctoral Thesis / Dissertation from the year 2008 in the subject Computer Science - Applied, grade: 1, University of Freiburg (Lehrstuhl für Mustererkennung und Bildverarbeitung), language: English, abstract: In this work, we present the problem of automatic appearance-based facial analysis with machine learning techniques and describe common specific sub-problems like face detection, facial feature detection and face recognition which are the crucial parts of many applications in the context of indexation, surveillance, access-control or human-computer interaction. To tackle this problem, we particularly focus on a technique called Convolutional Neural Network (CNN) which is inspired by biological evidence found in the visual cortex of mammalian brains and which has already been applied to many different classi fication problems. Existing CNN-based methods, like the face detection system proposed by Garcia and Delakis, show that this can be a very effective, efficient and robust approach to non-linear image processing tasks. An important step in many automatic facial analysis applications, e.g. face recognition, is face alignment which tries to translate, scale and rotate the face image such that specific facial features are roughly at predefined positions in the image. We propose an efficient approach to this problem using CNNs and experimentally show its very good performance on difficult test images. We further present a CNN-based method for automatic facial feature detection. The proposed system employs a hierarchical procedure which first roughly localizes the eyes, the nose and the mouth and then refines the result by detecting 10 different facial feature points. The detection rate of this method is 96% for the AR database and 87% for the BioID database tolerating an error of 10% of the inter-ocular distance. Finally, we propose a novel face recognition approach based on a specific CNN architecture learning a non-linear mapping of the image space into a lower-dimensional sub-space where the different classes are more easily separable. We applied this method to several public face databases and obtained better recognition rates than with classical face recognition approaches based on PCA or LDA. We also present a CNN-based method for the binary classification problem of gender recognition with face images and achieve a state-of-the-art accuracy. The results presented in this work show that CNNs perform very well on various facial image processing tasks, such as face alignment, facial feature detection and face recognition and clearly demonstrate that the CNN technique is a versatile, efficient and robust approach for facial image analysis.

Author: Jagdish Chand Bansal
Publisher: Springer
ISBN: 9811315957
Category : Technology & Engineering
Languages : en
Pages : 974

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Book Description
This two-volume book presents outcomes of the 7th International Conference on Soft Computing for Problem Solving, SocProS 2017. This conference is a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), the Indian Institute of Technology Roorkee, the South Asian University New Delhi and the National Institute of Technology Silchar, and brings together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions The book presents the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers in the areas including, but not limited to, algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It is a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task.

Author: Mohammad Esmaeel Mousa Pasandi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
This thesis focuses on the area of face processing and aims at designing a reliable framework to facilitate face, age, and gender recognition. A Bag-of-Words framework has been optimized for the task of face recognition by evaluating different feature descriptors and different bag-of-words configurations. More specifically, we choose a compact set of features (e.g., descriptors, window locations, window sizes, dictionary sizes, etc.) in order to produce the highest possible rate of accuracy. Experiments on a challenging dataset shows that our framework achieves a better level of accuracy when compared to other popular approaches such as dimension reduction techniques, edge detection operators, and texture and shape feature extractors. The second contribution of this thesis is the proposition of a general framework for age and gender classification. Although the vast majority of the existing solutions focus on a single visual descriptor that often only encodes a certain characteristic of the image regions, this thesis aims at integrating multiple feature types. For this purpose, feature selection is employed to obtain more accurate and robust facial descriptors. Once descriptors have been computed, a compact set of features is chosen, which facilitates facial image processing for age and gender analysis. In addition to this, a new color descriptor (CLR-LBP) is proposed and the results obtained is shown to be comparable to those of other pre-existing color descriptors. The experimental results indicates that our age and gender framework outperforms other proposed methods when examined on two challenging databases, where face objects are present with different expressions and levels of illumination. This achievement demonstrates the effectiveness of our proposed solution and allows us to achieve a higher accuracy over the existing state-of-the-art methods.

Author: Pradeep Kakkar
Publisher: GRIN Verlag
ISBN: 3668580065
Category : Computers
Languages : en
Pages : 103

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Book Description
Master's Thesis from the year 2017 in the subject Engineering - Computer Engineering, grade: 10, , course: M.Tech-ECE, language: English, abstract: Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. The rapidly expanding research in face processing is based on the premise that information about a user’s identity, state, and intent can be extracted from images and that computers can then react accordingly, e.g., by knowing person’s identity, person may be authenticated to utilize a particular service or not. A first step of any face processing system is registering the locations in images where faces are present. The local binary pattern is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. The LBP method can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Perhaps the most important property of the LBP operator in real-world applications is its invariance against monotonic gray level changes caused, e.g., by illumination variations. Another equally important is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. The success of LBP in face description is due to the discriminative power and computational simplicity of the LBP operator, and the robustness of LBP to mono-tonic gray scale changes caused by, for example, illumination variations. The use of histograms as features also makes the LBP approach robust to face misalignment and pose variations. For these reasons, the LBP methodology has already attained an established position in face analysis research. Because finding an efficient spatiotemporal representation for face analysis from videos is challenging, most of the existing works limit the scope of the problem by discarding the facial dynamics and only considering the structure. Motivated by the psychophysical findings which indicate that facial movements can provide valuable information to face analysis, spatiotemporal LBP approaches for face, facial expression and gender recognition from videos were described.

Author: Vincent G. Duffy
Publisher: Springer Science & Business Media
ISBN: 3642028098
Category : Computers
Languages : en
Pages : 775

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Book Description
The 13th International Conference on Human–Computer Interaction, HCI Inter- tional 2009, was held in San Diego, California, USA, July 19–24, 2009, jointly with the Symposium on Human Interface (Japan) 2009, the 8th International Conference on Engineering Psychology and Cognitive Ergonomics, the 5th International Conference on Universal Access in Human–Computer Interaction, the Third International Conf- ence on Virtual and Mixed Reality, the Third International Conference on Internati- alization, Design and Global Development, the Third International Conference on Online Communities and Social Computing, the 5th International Conference on Augmented Cognition, the Second International Conference on Digital Human Mod- ing, and the First International Conference on Human Centered Design. A total of 4,348 individuals from academia, research institutes, industry and gove- mental agencies from 73 countries submitted contributions, and 1,397 papers that were judged to be of high scientific quality were included in the program. These papers - dress the latest research and development efforts and highlight the human aspects of the design and use of computing systems. The papers accepted for presentation thoroughly cover the entire field of human–computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas.

Author: Stan Z. Li
Publisher: Springer Nature
ISBN: 3031435672
Category : Computers
Languages : en
Pages : 473

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Book Description
The history of computer-aided face recognition dates to the 1960s, yet the problem of automatic face recognition – a task that humans perform routinely and effortlessly in our daily lives – still poses great challenges, especially in unconstrained conditions. This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational recognition systems. After a thorough introduction, each subsequent chapter focuses on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Topics and features: Fully updated, revised, and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated detection and recognition systems Provides comprehensive coverage of face detection, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications Contains numerous step-by-step algorithms Describes a broad range of applications from person verification, surveillance, and security, to entertainment Presents contributions from an international selection of preeminent experts Integrates numerous supporting graphs, tables, charts, and performance data This practical and authoritative reference is an essential resource for researchers, professionals and students involved in image processing, computer vision, biometrics, security, Internet, mobile devices, human-computer interface, E-services, computer graphics and animation, and the computer game industry.

Author: Nalini K Ratha
Publisher: Springer Nature
ISBN: 3030746976
Category : Computers
Languages : en
Pages : 405

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Book Description
This book provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field. Even though there have been a number of different approaches proposed in the literature for face recognition based on deep learning methods, there is not a single book available in the literature that gives a complete overview of these methods. The proposed book captures the state of the art in face recognition using various deep learning methods, and it covers a variety of different topics related to face recognition. This book is aimed at graduate students studying electrical engineering and/or computer science. Biometrics is a course that is widely offered at both undergraduate and graduate levels at many institutions around the world: This book can be used as a textbook for teaching topics related to face recognition. In addition, the work is beneficial to practitioners in industry who are working on biometrics-related problems. The prerequisites for optimal use are the basic knowledge of pattern recognition, machine learning, probability theory, and linear algebra.