Realistic Relighting of 2D Facial Images Using Ratio Optimization

2007 ◽  
Author(s):  
Jong Won Yoon ◽  
Dong Hun Jwa ◽  
Jae Hyup Kim ◽  
Hyun Park ◽  
Young Shik Moon
Keyword(s):  
Ratio Optimization ◽  
Facial Images

Smiley faces in ads can lead to frowns at the cash register: Investigation of the impact of facial images on consumption

2009 ◽  
Author(s):  
Meng Zhu ◽  
Darron Billeter ◽  
Jeff Inman
Keyword(s):  
Cash Register ◽  
The Impact ◽  
Facial Images

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

Expectations about pain and analgesic treatment depend on medical providers’ facial appearances

2020 ◽  
Author(s):  
Elizabeth A. Necka ◽  
Carolyn Amir ◽  
Troy C. Dildine ◽  
Lauren Yvette Atlas
Keyword(s):  
Patient Outcomes ◽  
Visual Information ◽  
Healthcare Providers ◽  
Medical Procedures ◽  
Medical Providers ◽  
Analgesic Treatment ◽  
Digital Services ◽  
Receive Treatment ◽  
Facial Images ◽  
Trait Impressions

There is a robust link between patients’ expectations and clinical outcomes, as evidenced by the placebo effect. These expectations are shaped by the context surrounding treatment, including the patient-provider interaction. Prior work indicates that the provider’s behavior and characteristics, including warmth and competence, can shape patient outcomes. Yet humans rapidly form trait impressions of others prior to any in-person interaction. Here, we tested whether trait-impressions of hypothetical medical providers, based purely on facial images, influence participants’ choice of medical providers and expectations about their health following hypothetical medical procedures performed by those providers in a series of vignettes. Across five studies, participants selected providers who appeared more competent, based on facial visual information alone. Further, providers’ apparent competence predicted participants’ expectations about post-procedural pain and medication use. Participants’ perception of their similarity to providers also shaped expectations about pain and treatment outcomes. Our results suggest that humans develop expectations about their health outcomes prior to even setting foot in the clinic, based exclusively on first impressions. These findings have strong implications for health care, as individuals increasingly rely on digital services to choose healthcare providers, schedule appointments, and even receive treatment and care, a trend which is exacerbated as the world embraces telemedicine.

Accounts receivable LTV ratio optimization based on supply chain credit

2015 ◽  
Vol 32 (6) ◽  
pp. 652
Author(s):  
Ximei Sun ◽  
Bo Yu ◽  
Biao Han
Keyword(s):  
Supply Chain ◽  
Accounts Receivable ◽  
Ratio Optimization

A multi-scale context encoder for high quality restoration of facial images

2020 ◽  
Author(s):  
Trung Dung Do ◽  
Quoc Khanh Nguyen ◽  
Viet Hung Nguyen
Keyword(s):  
High Quality ◽  
Multi Scale ◽  
Facial Images

Mixing Ratio Optimization of Chaff Elements for Wideband Jamming Using PSO

2020 ◽  
Vol 19 (12) ◽  
pp. 2408-2412
Author(s):  
Yanchun Zuo ◽  
Lixin Guo ◽  
Wei Liu ◽  
Jianyang Ding
Keyword(s):  
Mixing Ratio ◽  
Ratio Optimization

scholarly journals Estimation of BMI from Facial Images using Semantic Segmentation based Region-Aware Pooling

2021 ◽  
pp. 104392
Author(s):  
Nadeem Yousaf ◽  
Sarfaraz Hussein ◽  
Waqas Sultani
Keyword(s):  
Semantic Segmentation ◽  
Facial Images

scholarly journals Distributed Adaptive Trace Ratio Optimization in Wireless Sensor Networks

2021 ◽  
pp. 1-1
Author(s):  
Cem Ates Musluoglu ◽  
Alexander Bertrand
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Trace Ratio ◽  
Ratio Optimization

scholarly journals Estimation of sex through morphometric landmark indices in facial images with strength of evidence in logistic regression analysis

2021 ◽  
pp. 100226
Author(s):  
Rajesh Verma ◽  
Navdha Bhardwaj ◽  
Pushap Deep Singh ◽  
Arnav Bhavsar ◽  
Vishal Sharma
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Strength Of Evidence ◽  
Facial Images

scholarly journals Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 195
Author(s):  
Adrian Sergiu Darabant ◽  
Diana Borza ◽  
Radu Danescu
Keyword(s):  
Race And Ethnicity ◽  
Large Scale ◽  
Recognition Rate ◽  
Social Signals ◽  
Image Characteristics ◽  
Race Effect ◽  
Race Ethnicity ◽  
The Impact ◽  
Facial Images ◽  
Ethnicity Classification

The human face holds a privileged position in multi-disciplinary research as it conveys much information—demographical attributes (age, race, gender, ethnicity), social signals, emotion expression, and so forth. Studies have shown that due to the distribution of ethnicity/race in training datasets, biometric algorithms suffer from “cross race effect”—their performance is better on subjects closer to the “country of origin” of the algorithm. The contributions of this paper are two-fold: (a) first, we gathered, annotated and made public a large-scale database of (over 175,000) facial images by automatically crawling the Internet for celebrities’ images belonging to various ethnicity/races, and (b) we trained and compared four state of the art convolutional neural networks on the problem of race and ethnicity classification. To the best of our knowledge, this is the largest, data-balanced, publicly-available face database annotated with race and ethnicity information. We also studied the impact of various face traits and image characteristics on the race/ethnicity deep learning classification methods and compared the obtained results with the ones extracted from psychological studies and anthropomorphic studies. Extensive tests were performed in order to determine the facial features to which the networks are sensitive to. These tests and a recognition rate of 96.64% on the problem of human race classification demonstrate the effectiveness of the proposed solution.

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