scholarly journals Face Identification from Manipulated Facial Images Using SIFT

2010 ◽  
Author(s):  
H R Chennamma ◽  
L Rangarajan ◽  
Veerabhadrappa
Keyword(s):  
Face Identification ◽  
Facial Images

scholarly journals FaceIDP: Face-IDentification Privacy under theCompressed Sensing Framework

2021 ◽  
Author(s):  
Lu Ou ◽  
Shaolin Liao ◽  
Zheng Qin ◽  
Yuan Hong ◽  
Dafang Zhang
Keyword(s):  
Differential Privacy ◽  
Real Life ◽  
Image Transmission ◽  
Face Identification ◽  
Facial Image ◽  
Scale Parameters ◽  
The Face ◽  
In The Wild ◽  
Privacy Budget ◽  
Facial Images

In FaceID era, large number of facial images could be used to breach the FaceID system, which demands effective FaceID privacy protection of the facial images for widespread adoption of FaceID technique. In this paper, to our best knowledge, we take the first step to systematically study such important FaceID privacy issue, under the framework of Compressed Sensing (CS) for fast facial image transmission. Specifically, we develop the Face-IDentification Privacy (FaceIDP) approach to protect the facial images from being used by the adversary to breach some FaceID system. First, a Dictionary Learning neural Network (DLNet) has been developed and trained with facial images database, to learn the common dictionary basis of the facial image database. Then, the encoding coefficients of the facial images are obtained. After that, the sanitizing noise is added to the encoding coefficients, which obfuscates the FaceID feature vector that is used to identify the FaceID. We have also proved that the FaceIDP is $\varepsilon$-differentially private. More importantly, optimal noise scale parameters have been obtained via the Lagrange Multiplier (LM) method to achieve better data utility for a given privacy budget $\varepsilon$. Finally, substantial experiments have been conducted to validate the efficiency of the FaceIDP with two real-life facial image databases, i.e., the LFW (Labeled Faces in the Wild) database and the PubFig database, and the results show that it outperforms other commonly used Differential Privacy (DP) approaches.

scholarly journals Liveness Detection in Face Identification Systems: Using Zernike Moments and Fresnel Transformation of Facial Images

2015 ◽  
Vol 8 (8) ◽  
pp. 523 ◽  
Author(s):  
Farhood Mousavizadeh ◽  
Keivan Maghooli ◽  
Emad Fatemizadeh ◽  
Mohammad Shahram Moin
Keyword(s):  
Zernike Moments ◽  
Face Identification ◽  
Liveness Detection ◽  
Facial Images

ALGORITHMS FOR IMAGE PRE-PROCESSING IN THE FACE IDENTIFICATION SYSTEM IN THE VIDEO STREAM

2019 ◽  
Vol 4 (91) ◽  
pp. 21-29 ◽  
Author(s):  
Yaroslav Trofimenko ◽  
Lyudmila Vinogradova ◽  
Evgeniy Ershov
Keyword(s):  
Video Stream ◽  
Face Identification ◽  
Identification System ◽  
The Face

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.

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