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USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY

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Authors:
R. Soundar Raja James, K. Naik and A. Nayak
Submitted On:
23 April 2018 - 12:51pm
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ICASSP2018-Poster-Albasir.v2.pdf

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[1] R. Soundar Raja James, K. Naik and A. Nayak, "USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3151. Accessed: May. 26, 2018.
@article{3151-18,
url = {http://sigport.org/3151},
author = {R. Soundar Raja James; K. Naik and A. Nayak },
publisher = {IEEE SigPort},
title = {USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY},
year = {2018} }
TY - EJOUR
T1 - USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY
AU - R. Soundar Raja James; K. Naik and A. Nayak
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3151
ER -
R. Soundar Raja James, K. Naik and A. Nayak. (2018). USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY. IEEE SigPort. http://sigport.org/3151
R. Soundar Raja James, K. Naik and A. Nayak, 2018. USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY. Available at: http://sigport.org/3151.
R. Soundar Raja James, K. Naik and A. Nayak. (2018). "USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY." Web.
1. R. Soundar Raja James, K. Naik and A. Nayak. USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3151

Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss


In this paper, we propose deep feature embedding learning for person re-identification (re-id) using lifted structured loss. Although triplet loss has been commonly used in deep neural networks for person re-id, the triplet loss-based framework is not effective in fully using the batch information. Thus, it needs to choose hard negative samples manually that is very time-consuming. To address this problem, we adopt lifted structured loss for deep neural networks that makes the network learn better feature embedding by minimizing intra-class variation and maximizing inter-class variation.

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Authors:
Zhangping He, Zhendong Zhang, Cheolkon Jung
Submitted On:
20 April 2018 - 5:23am
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ICASSP2018_PersonReID_final.pdf

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[1] Zhangping He, Zhendong Zhang, Cheolkon Jung, "Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3098. Accessed: May. 26, 2018.
@article{3098-18,
url = {http://sigport.org/3098},
author = {Zhangping He; Zhendong Zhang; Cheolkon Jung },
publisher = {IEEE SigPort},
title = {Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss},
year = {2018} }
TY - EJOUR
T1 - Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss
AU - Zhangping He; Zhendong Zhang; Cheolkon Jung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3098
ER -
Zhangping He, Zhendong Zhang, Cheolkon Jung. (2018). Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss. IEEE SigPort. http://sigport.org/3098
Zhangping He, Zhendong Zhang, Cheolkon Jung, 2018. Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss. Available at: http://sigport.org/3098.
Zhangping He, Zhendong Zhang, Cheolkon Jung. (2018). "Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss." Web.
1. Zhangping He, Zhendong Zhang, Cheolkon Jung. Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3098

Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid


We address the problem of constructing false data injection (FDI) attacks that can bypass the bad data detector (BDD) of a power grid. The attacker is assumed to have access to only power flow measurement data traces (collected over a limited period of time) and no other prior knowledge about the grid. Existing related algorithms are formulated under the assumption that the attacker has access to measurements collected over a long (asymptotically infinite) time period, which may not be realistic.

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Authors:
Fuxi Wen, David Yau
Submitted On:
19 April 2018 - 2:51pm
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Poster presentation

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[1] Fuxi Wen, David Yau, " Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3002. Accessed: May. 26, 2018.
@article{3002-18,
url = {http://sigport.org/3002},
author = {Fuxi Wen; David Yau },
publisher = {IEEE SigPort},
title = { Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid},
year = {2018} }
TY - EJOUR
T1 - Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid
AU - Fuxi Wen; David Yau
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3002
ER -
Fuxi Wen, David Yau. (2018). Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid. IEEE SigPort. http://sigport.org/3002
Fuxi Wen, David Yau, 2018. Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid. Available at: http://sigport.org/3002.
Fuxi Wen, David Yau. (2018). " Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid." Web.
1. Fuxi Wen, David Yau. Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3002

TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS


A common problem in audio forensics is the difficulty to
authenticate an audio recording. In this paper we provide a novel
and reliable solution to this problem by making use of a control
signal, visible and audible on the actual recording, yet ignored by
the listener, the TIC-TAC signal. We describe our live watermark
solution, we incorporate it in an integrity check algorithm and we
provide meaningful preliminary tests. Their results, computed in
terms of precision show an outstanding performance: 100%

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Authors:
A. Ciobanu
Submitted On:
14 April 2018 - 3:31pm
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ICASSP 2018 TIC TAC.pptx

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ICASSP 2018 TIC TAC.pptx

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[1] A. Ciobanu, "TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2848. Accessed: May. 26, 2018.
@article{2848-18,
url = {http://sigport.org/2848},
author = {A. Ciobanu },
publisher = {IEEE SigPort},
title = {TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS},
year = {2018} }
TY - EJOUR
T1 - TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS
AU - A. Ciobanu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2848
ER -
A. Ciobanu. (2018). TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS. IEEE SigPort. http://sigport.org/2848
A. Ciobanu, 2018. TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS. Available at: http://sigport.org/2848.
A. Ciobanu. (2018). "TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS." Web.
1. A. Ciobanu. TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2848

Continuous Security In IoT Using Blockchain


The two major roadblocks for state of the art Internet of Things (IoT) infrastructure like smart buildings, smart cities, etc. are lack of trust between various entities of system and single point of failure which is a vulnerability causing extreme damage to the whole system. This paper proposes a blockchain based IoT security solution where, trust is established through the immutable and decentralized nature of blockchain. The distributed nature of blockchain makes the system more robust and immune to single point of failure.

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Authors:
Pratik Verma, Dr. Aloknath De, Sai Anirudh Kondaveeti, Suman Shekhar
Submitted On:
13 April 2018 - 2:02am
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ICASSP2018Poster.pdf

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[1] Pratik Verma, Dr. Aloknath De, Sai Anirudh Kondaveeti, Suman Shekhar, "Continuous Security In IoT Using Blockchain", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2612. Accessed: May. 26, 2018.
@article{2612-18,
url = {http://sigport.org/2612},
author = {Pratik Verma; Dr. Aloknath De; Sai Anirudh Kondaveeti; Suman Shekhar },
publisher = {IEEE SigPort},
title = {Continuous Security In IoT Using Blockchain},
year = {2018} }
TY - EJOUR
T1 - Continuous Security In IoT Using Blockchain
AU - Pratik Verma; Dr. Aloknath De; Sai Anirudh Kondaveeti; Suman Shekhar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2612
ER -
Pratik Verma, Dr. Aloknath De, Sai Anirudh Kondaveeti, Suman Shekhar. (2018). Continuous Security In IoT Using Blockchain. IEEE SigPort. http://sigport.org/2612
Pratik Verma, Dr. Aloknath De, Sai Anirudh Kondaveeti, Suman Shekhar, 2018. Continuous Security In IoT Using Blockchain. Available at: http://sigport.org/2612.
Pratik Verma, Dr. Aloknath De, Sai Anirudh Kondaveeti, Suman Shekhar. (2018). "Continuous Security In IoT Using Blockchain." Web.
1. Pratik Verma, Dr. Aloknath De, Sai Anirudh Kondaveeti, Suman Shekhar. Continuous Security In IoT Using Blockchain [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2612

DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions


We introduce DigitalSeal, a transaction authentication tool that works in both online and offline use scenarios. DigitalSeal is a digital scanner that reads transaction information sent by an issuing entity of the DigitalSeal reader for authentication, and the information is encoded using a specially crafted bar-code. DigitalSeal views various pieces of transaction information for users to verify and proceed with transaction authentication. DigitalSeal is generic, and is capable of reading information viewed on paper, computer monitors (similarly, kiosk monitors), and mobile phones.

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Authors:
Changhun Jung, Jeonil Kang, Aziz Mohaisen, DaeHun Nyang
Submitted On:
19 April 2018 - 1:10pm
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[Presentation] DigitalSeal Presentation.pdf

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[1] Changhun Jung, Jeonil Kang, Aziz Mohaisen, DaeHun Nyang, "DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2534. Accessed: May. 26, 2018.
@article{2534-18,
url = {http://sigport.org/2534},
author = {Changhun Jung; Jeonil Kang; Aziz Mohaisen; DaeHun Nyang },
publisher = {IEEE SigPort},
title = {DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions},
year = {2018} }
TY - EJOUR
T1 - DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions
AU - Changhun Jung; Jeonil Kang; Aziz Mohaisen; DaeHun Nyang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2534
ER -
Changhun Jung, Jeonil Kang, Aziz Mohaisen, DaeHun Nyang. (2018). DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions. IEEE SigPort. http://sigport.org/2534
Changhun Jung, Jeonil Kang, Aziz Mohaisen, DaeHun Nyang, 2018. DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions. Available at: http://sigport.org/2534.
Changhun Jung, Jeonil Kang, Aziz Mohaisen, DaeHun Nyang. (2018). "DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions." Web.
1. Changhun Jung, Jeonil Kang, Aziz Mohaisen, DaeHun Nyang. DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2534

Optimal Online Cyberbullying Detection


Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbullying instances in an effort to mitigate the consequences. While techniques to automatically detect cyberbullying incidents have been developed, the scalability and timeliness of existing cyberbullying detection approaches have largely been ignored. We address this gap by formulating cyberbullying detection as a sequential hypothesis testing problem. Based on this formulation, we propose a novel

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Authors:
Daphney-Stavroula Zois, Angeliki Kapodistria, Mengfan Yao, Charalampos Chelmis
Submitted On:
12 April 2018 - 4:42pm
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ICASSP18_Poster_Zois_Kapodistria_Yao_Chelmis.pdf

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[1] Daphney-Stavroula Zois, Angeliki Kapodistria, Mengfan Yao, Charalampos Chelmis, "Optimal Online Cyberbullying Detection", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2499. Accessed: May. 26, 2018.
@article{2499-18,
url = {http://sigport.org/2499},
author = {Daphney-Stavroula Zois; Angeliki Kapodistria; Mengfan Yao; Charalampos Chelmis },
publisher = {IEEE SigPort},
title = {Optimal Online Cyberbullying Detection},
year = {2018} }
TY - EJOUR
T1 - Optimal Online Cyberbullying Detection
AU - Daphney-Stavroula Zois; Angeliki Kapodistria; Mengfan Yao; Charalampos Chelmis
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2499
ER -
Daphney-Stavroula Zois, Angeliki Kapodistria, Mengfan Yao, Charalampos Chelmis. (2018). Optimal Online Cyberbullying Detection. IEEE SigPort. http://sigport.org/2499
Daphney-Stavroula Zois, Angeliki Kapodistria, Mengfan Yao, Charalampos Chelmis, 2018. Optimal Online Cyberbullying Detection. Available at: http://sigport.org/2499.
Daphney-Stavroula Zois, Angeliki Kapodistria, Mengfan Yao, Charalampos Chelmis. (2018). "Optimal Online Cyberbullying Detection." Web.
1. Daphney-Stavroula Zois, Angeliki Kapodistria, Mengfan Yao, Charalampos Chelmis. Optimal Online Cyberbullying Detection [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2499

ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES

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Authors:
Kenta Iida, Hitoshi Kiya
Submitted On:
15 September 2017 - 9:56am
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PID2499.pdf

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[1] Kenta Iida, Hitoshi Kiya, "ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2105. Accessed: May. 26, 2018.
@article{2105-17,
url = {http://sigport.org/2105},
author = {Kenta Iida; Hitoshi Kiya },
publisher = {IEEE SigPort},
title = {ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES},
year = {2017} }
TY - EJOUR
T1 - ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES
AU - Kenta Iida; Hitoshi Kiya
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2105
ER -
Kenta Iida, Hitoshi Kiya. (2017). ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES. IEEE SigPort. http://sigport.org/2105
Kenta Iida, Hitoshi Kiya, 2017. ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES. Available at: http://sigport.org/2105.
Kenta Iida, Hitoshi Kiya. (2017). "ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES." Web.
1. Kenta Iida, Hitoshi Kiya. ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2105

ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION


Face and object recognition in uncontrolled scenarios due to pose and illumination variations, low resolution, etc. is a challenging research area. Here we propose a novel descriptor, Aligned Discriminative Pose Robust ( ADPR) descriptor, for matching faces and objects across pose which is also robust to resolution and illumination variations. We generate virtual intermediate pose subspaces from training examples at a few poses and compute the alignment matrices of those subspaces with the frontal subspace.

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Authors:
Soubhik Sanyal, Devraj Mandal, Soma Biswas
Submitted On:
14 September 2017 - 8:38am
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This is the presentation slides.

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[1] Soubhik Sanyal, Devraj Mandal, Soma Biswas, "ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2024. Accessed: May. 26, 2018.
@article{2024-17,
url = {http://sigport.org/2024},
author = {Soubhik Sanyal; Devraj Mandal; Soma Biswas },
publisher = {IEEE SigPort},
title = {ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION},
year = {2017} }
TY - EJOUR
T1 - ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION
AU - Soubhik Sanyal; Devraj Mandal; Soma Biswas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2024
ER -
Soubhik Sanyal, Devraj Mandal, Soma Biswas. (2017). ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION. IEEE SigPort. http://sigport.org/2024
Soubhik Sanyal, Devraj Mandal, Soma Biswas, 2017. ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION. Available at: http://sigport.org/2024.
Soubhik Sanyal, Devraj Mandal, Soma Biswas. (2017). "ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION." Web.
1. Soubhik Sanyal, Devraj Mandal, Soma Biswas. ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2024

Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics


Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

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Authors:
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi
Submitted On:
16 July 2016 - 11:13pm
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DCA_ICASSP16_Poster.pdf

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[1] Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/828. Accessed: May. 26, 2018.
@article{828-16,
url = {http://sigport.org/828},
author = {Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi },
publisher = {IEEE SigPort},
title = {Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics},
year = {2016} }
TY - EJOUR
T1 - Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics
AU - Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/828
ER -
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. IEEE SigPort. http://sigport.org/828
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, 2016. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. Available at: http://sigport.org/828.
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics." Web.
1. Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/828

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