Human Identification Based on Shallow Learning Using Facial Features

2019 ◽  
pp. 231-239
Author(s):  
Van-Dung Hoang ◽  
Cong-Hieu Le ◽  
The-Anh Pham
Keyword(s):  
Human Identification ◽  
Facial Features

HUMAN IDENTIFICATION BASED ON GAIT MODELING

2007 ◽  
Vol 04 (01) ◽  
pp. 27-38 ◽  
Author(s):  
BUFU HUANG ◽  
MENG CHEN ◽  
KA KEUNG LEE ◽  
YANGSHENG XU
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Human Identification ◽  
Performance Criterion ◽  
Human Gait ◽  
Facial Features ◽  
Machine Learning Method ◽  
Gait Performance ◽  
Dynamic Gait

Human gait is a dynamic biometrical feature which is complex and difficult to imitate. It is unique and more secure than static features such as passwords, fingerprints and facial features. In this paper, we present intelligent shoes for human identification based on human gait modeling and similarity evaluation with hidden Markov models (HMMs). Firstly we describe the intelligent shoe system for collecting human dynamic gait performance. Using the proposed machine learning method hidden Markov models, an individual wearer's gait model is derived and we then demonstrate the procedure for recognizing different wearers by analyzing the corresponding models. Next, we define a hidden-Markov-model-based similarity measure which allows us to evaluate resultant learning models. With the most likely performance criterion, it will help us to derive the similarity of individual behavior and its corresponding model. By utilizing human gait modeling and similarity evaluation based on hidden Markov models, the proposed method has produced satisfactory results for human identification during testing.

The Impact of Visual Stereotypes on Judgments about Rape

2016 ◽  
Vol 75 (3) ◽  
pp. 133-140
Author(s):  
Robert Busching ◽  
Johannes Lutz
Keyword(s):  
Mental Representations ◽  
Irrelevant Information ◽  
Clear Distinction ◽  
Rape Myth ◽  
Facial Features ◽  
Reverse Correlation ◽  
Correlation Technique ◽  
Specific Effects ◽  
Legal Judgments ◽  
The Impact

Abstract. Legally irrelevant information like facial features is used to form judgments about rape cases. Using a reverse-correlation technique, it is possible to visualize criminal stereotypes and test whether these representations influence judgments. In the first step, images of the stereotypical faces of a rapist, a thief, and a lifesaver were generated. These images showed a clear distinction between the lifesaver and the two criminal representations, but the criminal representations were rather similar. In the next step, the images were presented together with rape scenarios, and participants (N = 153) indicated the defendant’s level of liability. Participants with high rape myth acceptance scores attributed a lower level of liability to a defendant who resembled a stereotypical lifesaver. However, no specific effects of the image of the stereotypical rapist compared to the stereotypical thief were found. We discuss the findings with respect to the influence of visual stereotypes on legal judgments and the nature of these mental representations.

Face-ing who you are: Do racially prototypic facial features predict racial identification?

2014 ◽  
Author(s):  
Kathy Espino-Perez ◽  
Ryan Folliott ◽  
Brandon K. Brown ◽  
Debbie S. Ma
Keyword(s):  
Facial Features ◽  
Racial Identification

The face of a killer: How facial features influence the negation of guilt

2011 ◽  
Author(s):  
Lieke Curfs ◽  
Rob Holland ◽  
Jose Kerstholt ◽  
Daniel Wigboldus
Keyword(s):  
Facial Features ◽  
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.

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