Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
2,100
result(s) for
"Face Identification."
Sort by:
Craniofacial identification
\"The promotion of CCTV surveillance and identity cards, along with ever heightened security at airports, immigration control and institutional access, has seen a dramatic increase in the use of automated and manual recognition. In addition, several recent disasters have highlighted the problems and challenges associated with current disaster victim identification. Discussing the latest advances and key research into identification from the face and skull, this book draws together a wide range of elements relating to craniofacial analysis and identification. It examines all aspects of facial identification, including the determination of facial appearance from the skull, comparison of the skull with the face and the verification of living facial images. With sections covering the identification of the dead and of the living, it provides a valuable review of the current state of play along with the latest research advances in this constantly evolving field\"-- Provided by publisher.
Book
Our Biometric Future
Since the 1960s, a significant effort has been underway to program computers to see the human face to develop automated systems for identifying faces and distinguishing them from one another--commonly known as Facial Recognition Technology. While computer scientists are developing FRT in order to design more intelligent and interactive machines, businesses and states agencies view the technology as uniquely suited for smart surveillance - systems that automate the labor of monitoring in order to increase their efficacy and spread their reach.Tracking this technological pursuit, Our Biometric Future identifies FRT as a prime example of the failed technocratic approach to governance, where new technologies are pursued as shortsighted solutions to complex social problems. Culling news stories, press releases, policy statements, PR kits and other materials, Kelly Gates provides evidence that, instead of providing more security for more people, the pursuit of FRT is being driven by the priorities of corporations, law enforcement and state security agencies, all convinced of the technology's necessity and unhindered by its complicated and potentially destructive social consequences. By focusing on the politics of developing and deploying these technologies, Our Biometric Future argues not for the inevitability of a particular technological future, but for its profound contingency and contestability.
eBook
Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.
Journal Article
Craniofacial Identification
The promotion of CCTV surveillance and identity cards, along with ever heightened security at airports, immigration control and institutional access, has seen a dramatic increase in the use of automated and manual recognition. In addition, several recent disasters have highlighted the problems and challenges associated with current disaster victim identification. Discussing the latest advances and key research into identification from the face and skull, this book draws together a wide range of elements relating to craniofacial analysis and identification. It examines all aspects of facial identification, including the determination of facial appearance from the skull, comparison of the skull with the face and the verification of living facial images. With sections covering the identification of the dead and of the living, it provides a valuable review of the current state of play along with the latest research advances in this constantly evolving field.
eBook
Face dissimilarity judgments are predicted by representational distance in morphable and image-computable models
Human vision is attuned to the subtle differences between individual faces. Yet we lack a quantitative way of predicting how similar two face images look and whether they appear to show the same person. Principal component–based three-dimensional (3D) morphable models are widely used to generate stimuli in face perception research. These models capture the distribution of real human faces in terms of dimensions of physical shape and texture. How well does a “face space” based on these dimensions capture the similarity relationships humans perceive among faces? To answer this, we designed a behavioral task to collect dissimilarity and same/different identity judgments for 232 pairs of realistic faces. Stimuli sampled geometric relationships in a face space derived from principal components of 3D shape and texture (Basel face model [BFM]). We then compared a wide range of models in their ability to predict the data, including the BFM from which faces were generated, an active appearance model derived from face photographs, and image-computable models of visual perception. Euclidean distance in the BFM explained both dissimilarity and identity judgments surprisingly well. In a comparison against 16 diverse models, BFM distance was competitive with representational distances in state-of-the-art deep neural networks (DNNs), including novel DNNs trained on BFM synthetic identities or BFM latents. Models capturing the distribution of face shape and texture across individuals are not only useful tools for stimulus generation. They also capture important information about how faces are perceived, suggesting that human face representations are tuned to the statistical distribution of faces.
Journal Article
Face Recognition at a Distance for a Stand-Alone Access Control System
Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment.
Journal Article
Classical and modern face recognition approaches: a complete review
Human face recognition have been an active research area for the last few decades. Especially, during the last five years, it has gained significant research attention from multiple domains like computer vision, machine learning and artificial intelligence due to its remarkable progress and broad social applications. The primary goal of any face recognition system is to recognize the human identity from the static images, video data, data-streams and the knowledge of the context in which these data components are being actively used. In this review, we have highlighted major applications, challenges and trends of face recognition systems in social and scientific domains. The prime objective of this research is to sum-up recent face recognition techniques and develop a broad understanding of how these techniques behave on different datasets. Moreover, we discuss some key challenges such as variability in illumination, pose, aging, cosmetics, scale, occlusion, and background. Along with classical face recognition techniques, most recent research directions are deeply investigated, i.e., deep learning, sparse models and fuzzy set theory. Additionally, basic methodologies are briefly discussed, while contemporary research contributions are examined in broader details. Finally, this research presents future aspects of face recognition technologies and its potential significance in the upcoming digital society.
Journal Article
A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
Masked face recognition (MFR) has emerged as a critical domain in biometric identification, especially with the global COVID-19 pandemic, which introduced widespread face masks. This survey paper presents a comprehensive analysis of the challenges and advancements in recognizing and detecting individuals with masked faces, which has seen innovative shifts due to the necessity of adapting to new societal norms. Advanced through deep learning techniques, MFR, along with face mask recognition (FMR) and face unmasking (FU), represents significant areas of focus. These methods address unique challenges posed by obscured facial features, from fully to partially covered faces. Our comprehensive review explores the various deep learning-based methodologies developed for MFR, FMR, and FU, highlighting their distinctive challenges and the solutions proposed to overcome them. Additionally, we explore benchmark datasets and evaluation metrics specifically tailored for assessing performance in MFR research. The survey also discusses the substantial obstacles still facing researchers in this field and proposes future directions for the ongoing development of more robust and effective masked face recognition systems. This paper serves as an invaluable resource for researchers and practitioners, offering insights into the evolving landscape of face recognition technologies in the face of global health crises and beyond.
Journal Article
Efficient smart distributed face identification using the MixMaxSim decision function
Recognizing a large number of people is a common challenge in face identification applications, involving decreased accuracy, increased memory and time complexities. To address these issues, this study introduces a three-module approach: “toilers,” “affinity-meter,” and “decision-maker.” Unlike the random distribution methods used in previous solutions, this method employs clustering to distribute the problem into subnetworks called “toilers.” The toiler’s module calculates the likelihood of test data belonging to each class of each toiler, using the last layer outputs of deep learning models. Meanwhile, the affinity-meter module determines the similarity between the test data and the average of each class, employing a similarity measure. The decision-maker module combines the reports from the previous two modules and selects the final class, utilizing a mix of the max-max criterion and the similarity criterion. The proposed method outperforms existing solutions, achieving improved recall, precision, and F1-score. It effectively addresses memory, speed, and accuracy issues in face identification, surpassing both no-distribution and random methods on Glint360K, VGGFace2, and MS-Celeb-1M datasets. Overall, this method offers a more efficient and accurate approach by distributing the problem into subnetworks, demonstrating superior performance and scalability for large-scale face recognition applications.
Journal Article