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Brain Reading Using Full Brain Support Vector Machines for Object Recognition: There Is No "Face" Identification Area
2008
Neural Computation
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Consequently, the claim that there are areas of the brain that are necessary and sufficient for object identification cannot be resolved with existing associative methods (e.g. ...
This performance allowed us to reliably and uniquely assay the classifier's voxel diagnosticity in all individual subject's brains: In this two class case there may be specific areas diagnostic for HOUSE ...
We are also grateful for generous support from from the McDonnell Foundation and NSF. ...
doi:10.1162/neco.2007.09-06-340
pmid:18047411
fatcat:debpyjybfnamfoerhj2jepqqhi
Face recognition
2003
ACM Computing Surveys
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of machine recognition of faces. ...
There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies ...
The first really successful demonstration of machine recognition of faces was made in [Turk and Pentland 1991] using eigenpictures (also known as eigenfaces) for face detection and identification. ...
doi:10.1145/954339.954342
fatcat:3bx4i7gsjvbrbguiv4u2jekytu
Face Recognition System
[article]
2019
arXiv
pre-print
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Deep learning is one of the new and important branches in machine learning. ...
Deep Learning is a framework that contains several important algorithms. For different applications (images, voice, text), you need to use different network models to achieve better results. ...
If there is no face detected, the image will be discarded. ...
arXiv:1901.02452v1
fatcat:7orsmcfomzbdjmqnuyhi2mgj64
Face Detection & Recognition using Tensor Flow: A Review
2018
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
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It is clear that there are many applications the uses for facial recognition systems. ...
Most of these forms currently do not use face recognition as the standard form of granting entry, but with advancing technologies in computers along with more refined algorithms, facial recognition is ...
Face recognition is one of the important methods of biometric identification. ...
doi:10.24297/ijct.v18i0.7924
fatcat:hzxzf4lslzgkhly6nhcn5o5ywu
Combined Classifiers for Invariant Face Recognition
[article]
2016
arXiv
pre-print
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In this work, a system for combining unstable, low performance classifiers is proposed. The system is applied to face images of 392 persons. ...
No single classifier can alone solve the complex problem of face recognition. Researchers found that combining some base classifiers usually enhances their recognition rate. ...
In this work, the term face recognition is used mainly to refer to face identification. ...
arXiv:1607.06973v1
fatcat:ko5tl4ez4na5zmayukgi6cwzui
Face Recognition and Skin Extraction under a Dynamic Video
2018
Robotics & Automation Engineering Journal
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By using Face ++ interface for face and skin recognition experiment, after some tests, in the case of a good picture resolution, the recognition rate is about 70%; the skin color extraction is one kind ...
Afterwards, face recognition and skin pigmentation are extracted and detailed. ...
Acknowledgement This research was supported by HuaQiao University, Fujian, P.R. China under the HuaQiao Scientific Research Foundation for Talents plan. ...
doi:10.19080/raej.2018.03.555602
fatcat:c3zsbtcbtbgphpjm3dv74uykm4
Symmetry, probability, and recognition in face space
2009
Proceedings of the National Academy of Sciences of the United States of America
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Evidence is presented that the dimension of face recognition space for human faces is dramatically lower than previous estimates. ...
Another is a recognition algorithm that by reasonable criteria is nearly 100% accurate. face dimension ͉ probability distributions ͉ face recognition ...
We thank Bruce Knight, Ehud Kaplan, and Yu Zhang for reading, commenting on, and making other helpful contributions to the manuscript. ...
doi:10.1073/pnas.0812680106
pmid:19365075
pmcid:PMC2678476
fatcat:4ikbqs5irjbu3dvyvshy7php7y
Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet
2012
Frontiers in Computational Neuroscience
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Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. ...
Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. ...
Watt, of Stirling University, is thanked for assistance with the implementation of the difference of Gaussian filters used in many experiments with VisNet and VisNet2. ...
doi:10.3389/fncom.2012.00035
pmid:22723777
pmcid:PMC3378046
fatcat:vywfnubxvzaptmjdhzazbvjsre
Deep Convolutional Neural Network-Based Approaches for Face Recognition
2019
Applied Sciences
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This paper investigates the performance of the pre-trained CNN with multi-class support vector machine (SVM) classifier and the performance of transfer learning using the AlexNet model to perform classification ...
Face recognition (FR) is defined as the process through which people are identified using facial images. ...
Conflicts of Interest: There are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome ...
doi:10.3390/app9204397
fatcat:otjlvzfjqfc57akbnmnb4xwcqe
Toward a unified model of face and object recognition in the human visual system
2013
Frontiers in Psychology
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Keywords: face recognition, object recognition, learning and memory, holistic processing, neural network modeling www.frontiersin.org August 2013 | Volume 4 | Article 497 | 1 Wallis Unifying face and object ...
During this period, accumulating evidence has led many scientists to conclude that objects and faces are recognised in fundamentally distinct ways, and in fundamentally distinct cortical areas. ...
others have argued that there is no specialist region for face processing per se. ...
doi:10.3389/fpsyg.2013.00497
pmid:23966963
pmcid:PMC3744012
fatcat:f6yx74ju5nhmncpmirulgdsevq
Closing the gap between single-unit and neural population codes: Insights from deep learning in face recognition
2021
Journal of Vision
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To bridge this gap, we studied the relationship between single-unit and ensemble codes for identity, gender, and viewpoint, using a deep convolutional neural network (DCNN) trained for face recognition ...
Identification was remarkably accurate using random samples of only 3% of the network's output units, and all units had substantial identity-predicting power. ...
This research is based in part on work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2014 ...
doi:10.1167/jov.21.8.15
pmid:34379084
pmcid:PMC8363775
fatcat:zskpuoaolze7vjcygt46hntjai
Emotion Detection of Human Face
2019
International Journal of Engineering and Advanced Technology
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The main aim is to read the facial expressions of the human beings using a good resolution camera so that the machine can identify the human sentiments. ...
The Facial expressions are recorded by the use of camera which is attached to user device. ...
The full process of emotion detection of human face has been done successfully. The accuracy is around 95%. Areas of Specialization for such analysis are numerous and varied. ...
doi:10.35940/ijeat.a2070.109119
fatcat:ieqbd2f6yna7jde3rfougmlg34
For Now We See through an AI Darkly; but Then Face-to-Face: A Brief Survey of Emotion Recognition in Biometric Art
2020
Przegląd Kulturoznawczy
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And the use of technological labor by corporate, government, and institutional agents for extracting data capital from both the static morphology of the face and dynamic movement of the emotions is accelerating ...
such art and the institutions that support it, as well as how this biometric art is made and what it is about. ...
is no place for disability. 15 This exclusion is deeply felt by disabled readers and writers of science fiction: "I grew up reading science fiction and I found no mirrors. ...
doi:10.4467/20843860pk.20.025.12585
fatcat:ccsfdy2bszcx5eyfg2vzp2dbcy
Invariant visual object recognition: biologically plausible approaches
2015
Biological cybernetics
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Acknowledgments The use of the ORL database of faces http://www. cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html) provided by AT&T Laboratories Cambridge is acknowledged. ...
We also acknowledge the use of Blender software (http://www.blender.org) to render the 3D objects, of the CalTech-256 (Griffin et al. 2007 ), and of the Amsterdam Library of Images (ALOI) (Geusebroek ...
Indeed, the results shown in Sect. 3.1.2 were obtained with support vector machine decoding used for both HMAX and VisNet. ...
doi:10.1007/s00422-015-0658-2
pmid:26335743
pmcid:PMC4572081
fatcat:fwhibbpoyrdrlj5svto72rq6dm
Identity Recognition Using Biological Electroencephalogram Sensors
2016
Journal of Sensors
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Further, we evaluate the security and practicality of using brain wave in identity recognition and anticounterfeiting authentication and describe use cases of several machine learning methods in brain ...
Finally, we propose several brain wave-based identity recognition techniques for further studies and conclude this paper. ...
The experimental results show that the best identification rate is 97.25% by using a Support Vector Machine classifier. Furthermore, the classification accuracy achieves 85% in experiments. ...
doi:10.1155/2016/1831742
fatcat:lftejeohsvd35hdw54uewwpuqa
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