AutoAlbum: clustering digital photographs using probabilistic model merging

2000 ◽  
Author(s):  
Platt
Keyword(s):  
Probabilistic Model ◽  
Digital Photographs ◽  
Model Merging

Facial recognition technologies and the new physiognomic era

2020 ◽  
Vol 43 (2) ◽  
pp. 45-56
Author(s):  
Abigail Nieves Delgado
Keyword(s):  
Facial Recognition ◽  
Personal Identification ◽  
Digital Photographs ◽  
Different Types ◽  
Recognition Systems ◽  
The Way

The current overproduction of images of faces in digital photographs and videos, and the widespread use of facial recognition technologies have important effects on the way we understand ourselves and others. This is because facial recognition technologies create new circulation pathways of images that transform portraits and photographs into material for potential personal identification. In other words, different types of images of faces become available to the scrutiny of facial recognition technologies. In these new circulation pathways, images are continually shared between many different actors who use (or abuse) them for different purposes. Besides this distribution of images, the categorization practices involved in the development and use of facial recognition systems reinvigorate physiognomic assumptions and judgments (e.g., about beauty, race, dangerousness). They constitute the framework through which faces are interpreted. This paper shows that, because of this procedure, facial recognition technologies introduce new and far-reaching »facialization« processes, which reiterate old discriminatory practices.

Assessment and Analysis of Credit Risk with Using of Logical and Probabilistic Model

2002 ◽  
Author(s):  
Vassilij Karassev ◽  
Andrey Roukine ◽  
E.D. Dmitrievich Solojentsev
Keyword(s):  
Credit Risk ◽  
Probabilistic Model

scholarly journals Joint Semantic Synthesis and Morphological Analysis of the Derived Word

2018 ◽  
Vol 6 ◽  
pp. 33-48 ◽  
Author(s):  
Ryan Cotterell ◽  
Hinrich Schütze
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Probabilistic Model ◽  
Morphological Analysis ◽  
Semantic Representation ◽  
English Word ◽  
Word Formation ◽  
Additive Models ◽  
Segmentation Accuracy ◽  
Morphological Productivity

Much like sentences are composed of words, words themselves are composed of smaller units. For example, the English word questionably can be analyzed as question+ able+ ly. However, this structural decomposition of the word does not directly give us a semantic representation of the word’s meaning. Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts. In this work, we propose a novel probabilistic model of word formation that captures both the analysis of a word w into its constituent segments and the synthesis of the meaning of w from the meanings of those segments. Our model jointly learns to segment words into morphemes and compose distributional semantic vectors of those morphemes. We experiment with the model on English CELEX data and German DErivBase (Zeller et al., 2013) data. We show that jointly modeling semantics increases both segmentation accuracy and morpheme F1 by between 3% and 5%. Additionally, we investigate different models of vector composition, showing that recurrent neural networks yield an improvement over simple additive models. Finally, we study the degree to which the representations correspond to a linguist’s notion of morphological productivity.

scholarly journals Accuracy and Reproducibility of Facial Measurements of Digital Photographs and Wrapped Cone Beam Computed Tomography (CBCT) Photographs

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 757
Author(s):  
Maged Sultan Alhammadi ◽  
Abeer Abdulkareem Al-mashraqi ◽  
Rayid Hussain Alnami ◽  
Nawaf Mohammad Ashqar ◽  
Omar Hassan Alamir ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Soft Tissue ◽  
Cone Beam Computed Tomography ◽  
High Reliability ◽  
Three Dimensional ◽  
Cone Beam ◽  
Cross Sectional ◽  
Facial Profile ◽  
Digital Photographs ◽  
Gonial Angle

The study sought to assess whether the soft tissue facial profile measurements of direct Cone Beam Computed Tomography (CBCT) and wrapped CBCT images of non-standardized facial photographs are accurate compared to the standardized digital photographs. In this cross-sectional study, 60 patients with an age range of 18–30 years, who were indicated for CBCT, were enrolled. Two facial photographs were taken per patient: standardized and random (non-standardized). The non-standardized ones were wrapped with the CBCT images. The most used soft tissue facial profile landmarks/parameters (linear and angular) were measured on direct soft tissue three-dimensional (3D) images and on the photographs wrapped over the 3D-CBCT images, and then compared to the standardized photographs. The reliability analysis was performed using concordance correlation coefficients (CCC) and depicted graphically using Bland–Altman plots. Most of the linear and angular measurements showed high reliability (0.91 to 0.998). Nevertheless, four soft tissue measurements were unreliable; namely, posterior gonial angle (0.085 and 0.11 for wrapped and direct CBCT soft tissue, respectively), mandibular plane angle (0.006 and 0.0016 for wrapped and direct CBCT soft tissue, respectively), posterior facial height (0.63 and 0.62 for wrapped and direct CBCT soft tissue, respectively) and total soft tissue facial convexity (0.52 for both wrapped and direct CBCT soft tissue, respectively). The soft tissue facial profile measurements from either the direct 3D-CBCT images or the wrapped CBCT images of non-standardized frontal photographs were accurate, and can be used to analyze most of the soft tissue facial profile measurements.

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