BookDOI
Handbook of Face Recognition
Stan Z. Li,Anil K. Jain +1 more
TLDR
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 face recognition systems, as well as offering challenges and future directions.Abstract:
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 face recognition systems. After a thorough introductory chapter, each of the following chapters focus on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Features: fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems; provides comprehensive coverage of face detection, tracking, 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; presents contributions from an international selection of experts; integrates numerous supporting graphs, tables, charts, and performance data.read more
Citations
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Journal ArticleDOI
Face Description with Local Binary Patterns: Application to Face Recognition
TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Proceedings ArticleDOI
Information revelation and privacy in online social networks
Ralph Gross,Alessandro Acquisti +1 more
TL;DR: This paper analyzes the online behavior of more than 4,000 Carnegie Mellon University students who have joined a popular social networking site catered to colleges and evaluates the amount of information they disclose and study their usage of the site's privacy settings.
Journal ArticleDOI
Facial expression recognition based on Local Binary Patterns: A comprehensive study
TL;DR: This paper empirically evaluates facial representation based on statistical local features, Local Binary Patterns, for person-independent facial expression recognition, and observes that LBP features perform stably and robustly over a useful range of low resolutions of face images, and yield promising performance in compressed low-resolution video sequences captured in real-world environments.
Journal ArticleDOI
Multi-PIE
TL;DR: This paper introduces the database, describes the recording procedure, and presents results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE.
Book
Social Signal Processing
TL;DR: It is argued that next-generation computing needs to include the essence of social intelligence - the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement - in order to become more effective and more efficient.
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Proceedings ArticleDOI
Rapid object detection using a boosted cascade of simple features
Paul A. Viola,Michael Jones +1 more
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Proceedings ArticleDOI
Object recognition from local scale-invariant features
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.