scholarly journals A novel camera trapping method for individually identifying pumas by facial features

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Peter D. Alexander ◽  
Derek J. Craighead
Keyword(s):  
Camera Trapping ◽  
Facial Features ◽  
Trapping Method

scholarly journals A baited-camera trapping method for estimating the size and sex structure of African leopard (Panthera pardus) populations

MethodsX ◽  
2020 ◽  
Vol 7 ◽  
pp. 101042
Author(s):  
Christoffel J. Joubert ◽  
Allan Tarugara ◽  
Bruce W. Clegg ◽  
Edson Gandiwa ◽  
Victor K. Muposhi
Keyword(s):  
Camera Trapping ◽  
Panthera Pardus ◽  
Sex Structure ◽  
Trapping Method

The effectiveness and cost of camera traps for surveying small reptiles and critical weight range mammals: a comparison with labour-intensive complementary methods

2015 ◽  
Vol 42 (5) ◽  
pp. 414 ◽  
Author(s):  
Dustin J. Welbourne ◽  
Christopher MacGregor ◽  
David Paull ◽  
David B. Lindenmayer
Keyword(s):  
Monitoring Program ◽  
Camera Traps ◽  
Camera Trapping ◽  
Mammal Species ◽  
Critical Weight ◽  
Complementary Methods ◽  
Incidence Data ◽  
Trapping Method ◽  
The Difference

Context Biodiversity studies often require wildlife researchers to survey multiple species across taxonomic classes. To detect terrestrial squamate and mammal species, often multiple labour-intensive survey techniques are required. Camera traps appear to be more effective and cost-efficient than labour-intensive methods for detecting some mammal species. Recent developments have seen camera traps used for detecting terrestrial squamates. However, the performance of camera traps to survey terrestrial squamate and mammal species simultaneously has not been evaluated. Aim We compared the effectiveness and financial cost of a camera trapping method capable of detecting small squamates and mammals with a set of labour-intensive complementary methods, which have been used in a long-term monitoring program. Methods We compared two survey protocols: one employed labour-intensive complementary methods consisting of cage traps, Elliott traps and artificial refuges; the second utilised camera traps. Comparisons were made of the total number of species detected, species detectability, and cost of executing each type of survey. Key results Camera traps detected significantly more target species per transect than the complementary methods used. Although camera traps detected more species of reptile per transect, the difference was not significant. For the initial survey, camera traps were more expensive than the complementary methods employed, but for realistic cost scenarios camera traps were less expensive in the long term. Conclusions Camera traps are more effective and less expensive than the complementary methods used for acquiring incidence data on terrestrial squamate and mammal species. Implications The camera trapping method presented does not require customised equipment; thus, wildlife managers can use existing camera trapping equipment to detect cryptic mammal and squamate species simultaneously.

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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