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Biometrics

Image Quality Assessment to Enhance Infrared Face Recognition


Automatic quality evaluation of infrared images has not been researched as extensively as for images of the visible spectrum. Moreover, there is a lack of studies on the influence of degradation of image quality on the performance of computer vision tasks operating on thermal images. Here, we quantify the impact of common image distortions on infrared face recognition, and present a method for aggregating perceptual quality-aware features to improve the identification rates.

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Authors:
Camilo G. Rodríguez Pulecio, Hernan D. Benítez-Restrepo, Alan C. Bovik
Submitted On:
18 September 2017 - 8:55am
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ICIP 2017 Presentation Slides

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[1] Camilo G. Rodríguez Pulecio, Hernan D. Benítez-Restrepo, Alan C. Bovik, "Image Quality Assessment to Enhance Infrared Face Recognition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2223. Accessed: Oct. 19, 2017.
@article{2223-17,
url = {http://sigport.org/2223},
author = {Camilo G. Rodríguez Pulecio; Hernan D. Benítez-Restrepo; Alan C. Bovik },
publisher = {IEEE SigPort},
title = {Image Quality Assessment to Enhance Infrared Face Recognition},
year = {2017} }
TY - EJOUR
T1 - Image Quality Assessment to Enhance Infrared Face Recognition
AU - Camilo G. Rodríguez Pulecio; Hernan D. Benítez-Restrepo; Alan C. Bovik
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2223
ER -
Camilo G. Rodríguez Pulecio, Hernan D. Benítez-Restrepo, Alan C. Bovik. (2017). Image Quality Assessment to Enhance Infrared Face Recognition. IEEE SigPort. http://sigport.org/2223
Camilo G. Rodríguez Pulecio, Hernan D. Benítez-Restrepo, Alan C. Bovik, 2017. Image Quality Assessment to Enhance Infrared Face Recognition. Available at: http://sigport.org/2223.
Camilo G. Rodríguez Pulecio, Hernan D. Benítez-Restrepo, Alan C. Bovik. (2017). "Image Quality Assessment to Enhance Infrared Face Recognition." Web.
1. Camilo G. Rodríguez Pulecio, Hernan D. Benítez-Restrepo, Alan C. Bovik. Image Quality Assessment to Enhance Infrared Face Recognition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2223

PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN


Person re-identification that aims at matching individuals across multiple camera views has become indispensable in intelligent video surveillance systems. It remains challenging due to the large variations of pose, illumination, occlusion and camera viewpoint. Feature representation and metric learning are the two fundamental components in person re-identification.

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Authors:
Lianghua Duan, Na Yang, Junyu Dong
Submitted On:
15 September 2017 - 7:39am
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ICIP2017-2955.pdf

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[1] Lianghua Duan, Na Yang, Junyu Dong, "PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2125. Accessed: Oct. 19, 2017.
@article{2125-17,
url = {http://sigport.org/2125},
author = {Lianghua Duan; Na Yang; Junyu Dong },
publisher = {IEEE SigPort},
title = {PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN},
year = {2017} }
TY - EJOUR
T1 - PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN
AU - Lianghua Duan; Na Yang; Junyu Dong
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2125
ER -
Lianghua Duan, Na Yang, Junyu Dong. (2017). PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN. IEEE SigPort. http://sigport.org/2125
Lianghua Duan, Na Yang, Junyu Dong, 2017. PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN. Available at: http://sigport.org/2125.
Lianghua Duan, Na Yang, Junyu Dong. (2017). "PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN." Web.
1. Lianghua Duan, Na Yang, Junyu Dong. PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2125

PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN


Person re-identification that aims at matching individuals across multiple camera views has become indispensable in intelligent video surveillance systems. It remains challenging due to the large variations of pose, illumination, occlusion and camera viewpoint. Feature representation and metric learning are the two fundamental components in person re-identification.

Paper Details

Authors:
Lianghua Duan, Na Yang, Junyu Dong
Submitted On:
15 September 2017 - 7:39am
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ICIP2017-2955.pdf

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[1] Lianghua Duan, Na Yang, Junyu Dong, "PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2124. Accessed: Oct. 19, 2017.
@article{2124-17,
url = {http://sigport.org/2124},
author = {Lianghua Duan; Na Yang; Junyu Dong },
publisher = {IEEE SigPort},
title = {PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN},
year = {2017} }
TY - EJOUR
T1 - PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN
AU - Lianghua Duan; Na Yang; Junyu Dong
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2124
ER -
Lianghua Duan, Na Yang, Junyu Dong. (2017). PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN. IEEE SigPort. http://sigport.org/2124
Lianghua Duan, Na Yang, Junyu Dong, 2017. PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN. Available at: http://sigport.org/2124.
Lianghua Duan, Na Yang, Junyu Dong. (2017). "PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN." Web.
1. Lianghua Duan, Na Yang, Junyu Dong. PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2124

FACE ANTI-SPOOFING VIA DEEP LOCAL BINARY PATTERNS

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Authors:
Xiaoyi Feng, Xiaoyue Jiang, Zhaoqiang Xia, Abdenour Hadid
Submitted On:
15 September 2017 - 3:51am
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FaceSpoofing4ICIP2017

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[1] Xiaoyi Feng, Xiaoyue Jiang, Zhaoqiang Xia, Abdenour Hadid, "FACE ANTI-SPOOFING VIA DEEP LOCAL BINARY PATTERNS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2093. Accessed: Oct. 19, 2017.
@article{2093-17,
url = {http://sigport.org/2093},
author = {Xiaoyi Feng; Xiaoyue Jiang; Zhaoqiang Xia; Abdenour Hadid },
publisher = {IEEE SigPort},
title = {FACE ANTI-SPOOFING VIA DEEP LOCAL BINARY PATTERNS},
year = {2017} }
TY - EJOUR
T1 - FACE ANTI-SPOOFING VIA DEEP LOCAL BINARY PATTERNS
AU - Xiaoyi Feng; Xiaoyue Jiang; Zhaoqiang Xia; Abdenour Hadid
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2093
ER -
Xiaoyi Feng, Xiaoyue Jiang, Zhaoqiang Xia, Abdenour Hadid. (2017). FACE ANTI-SPOOFING VIA DEEP LOCAL BINARY PATTERNS. IEEE SigPort. http://sigport.org/2093
Xiaoyi Feng, Xiaoyue Jiang, Zhaoqiang Xia, Abdenour Hadid, 2017. FACE ANTI-SPOOFING VIA DEEP LOCAL BINARY PATTERNS. Available at: http://sigport.org/2093.
Xiaoyi Feng, Xiaoyue Jiang, Zhaoqiang Xia, Abdenour Hadid. (2017). "FACE ANTI-SPOOFING VIA DEEP LOCAL BINARY PATTERNS." Web.
1. Xiaoyi Feng, Xiaoyue Jiang, Zhaoqiang Xia, Abdenour Hadid. FACE ANTI-SPOOFING VIA DEEP LOCAL BINARY PATTERNS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2093

DEEP MULTI-TASK LEARNING FOR GAIT-BASED BIOMETRICS


The task of identifying people by the way they walk is known as ‘gait recognition’. Although gait is mainly used for identification, additional tasks as gender recognition or age estimation may be addressed based on gait as well. In such cases, traditional approaches consider those tasks as independent ones, defining separated task-specific features and models for them.

Paper Details

Authors:
M.J. Marin-Jimenez, F.M. Castro, N. Guil, F. de la Torre, R. Medina-Carnicer
Submitted On:
14 September 2017 - 9:42am
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Presentation

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[1] M.J. Marin-Jimenez, F.M. Castro, N. Guil, F. de la Torre, R. Medina-Carnicer, "DEEP MULTI-TASK LEARNING FOR GAIT-BASED BIOMETRICS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2032. Accessed: Oct. 19, 2017.
@article{2032-17,
url = {http://sigport.org/2032},
author = {M.J. Marin-Jimenez; F.M. Castro; N. Guil; F. de la Torre; R. Medina-Carnicer },
publisher = {IEEE SigPort},
title = {DEEP MULTI-TASK LEARNING FOR GAIT-BASED BIOMETRICS},
year = {2017} }
TY - EJOUR
T1 - DEEP MULTI-TASK LEARNING FOR GAIT-BASED BIOMETRICS
AU - M.J. Marin-Jimenez; F.M. Castro; N. Guil; F. de la Torre; R. Medina-Carnicer
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2032
ER -
M.J. Marin-Jimenez, F.M. Castro, N. Guil, F. de la Torre, R. Medina-Carnicer. (2017). DEEP MULTI-TASK LEARNING FOR GAIT-BASED BIOMETRICS. IEEE SigPort. http://sigport.org/2032
M.J. Marin-Jimenez, F.M. Castro, N. Guil, F. de la Torre, R. Medina-Carnicer, 2017. DEEP MULTI-TASK LEARNING FOR GAIT-BASED BIOMETRICS. Available at: http://sigport.org/2032.
M.J. Marin-Jimenez, F.M. Castro, N. Guil, F. de la Torre, R. Medina-Carnicer. (2017). "DEEP MULTI-TASK LEARNING FOR GAIT-BASED BIOMETRICS." Web.
1. M.J. Marin-Jimenez, F.M. Castro, N. Guil, F. de la Torre, R. Medina-Carnicer. DEEP MULTI-TASK LEARNING FOR GAIT-BASED BIOMETRICS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2032

AN EFFICIENT DEEP NEURAL NETWORKS TRAINING FRAMEWORK FOR ROBUST FACE RECOGNITION


In recent years, the triplet loss-based deep neural networks (DNN) are widely used in the task of face recognition and achieve the state-of-the-art performance. However, the complexity of training the triplet loss-based DNN is significantly high due to the difficulty in generating high-quality training samples. In this paper, we propose a novel DNN training framework to accelerate the training process of the triplet loss-based DNN and meanwhile to improve the performance of face recognition. More specifically, the proposed framework contains two stages: 1) The DNN initialization.

Paper Details

Authors:
Canping Su, Yan Yan, Si Chen, Hanzi Wang
Submitted On:
13 September 2017 - 1:47am
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Face recognition

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[1] Canping Su, Yan Yan, Si Chen, Hanzi Wang, "AN EFFICIENT DEEP NEURAL NETWORKS TRAINING FRAMEWORK FOR ROBUST FACE RECOGNITION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1962. Accessed: Oct. 19, 2017.
@article{1962-17,
url = {http://sigport.org/1962},
author = {Canping Su; Yan Yan; Si Chen; Hanzi Wang },
publisher = {IEEE SigPort},
title = {AN EFFICIENT DEEP NEURAL NETWORKS TRAINING FRAMEWORK FOR ROBUST FACE RECOGNITION},
year = {2017} }
TY - EJOUR
T1 - AN EFFICIENT DEEP NEURAL NETWORKS TRAINING FRAMEWORK FOR ROBUST FACE RECOGNITION
AU - Canping Su; Yan Yan; Si Chen; Hanzi Wang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1962
ER -
Canping Su, Yan Yan, Si Chen, Hanzi Wang. (2017). AN EFFICIENT DEEP NEURAL NETWORKS TRAINING FRAMEWORK FOR ROBUST FACE RECOGNITION. IEEE SigPort. http://sigport.org/1962
Canping Su, Yan Yan, Si Chen, Hanzi Wang, 2017. AN EFFICIENT DEEP NEURAL NETWORKS TRAINING FRAMEWORK FOR ROBUST FACE RECOGNITION. Available at: http://sigport.org/1962.
Canping Su, Yan Yan, Si Chen, Hanzi Wang. (2017). "AN EFFICIENT DEEP NEURAL NETWORKS TRAINING FRAMEWORK FOR ROBUST FACE RECOGNITION." Web.
1. Canping Su, Yan Yan, Si Chen, Hanzi Wang. AN EFFICIENT DEEP NEURAL NETWORKS TRAINING FRAMEWORK FOR ROBUST FACE RECOGNITION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1962

DEFORMABLE MULTI-SCALE SCHEME FOR BIOMETRIC PERSONAL IDENTIFICATION


Human identification has now been a social liability due to
frequent terror threats and corrupt bureaucratic practices,
especially in rural countries like India. It has been surprisingly
observed that fingerprint quality is poor as compared
with finger knuckle quality of rural users as they exist on
the outer hand side. In this paper, we are proposing a novel
finger-knuckle-print based identification system. Initially,
finger knuckle image is preprocessed using proposed local
and adaptive image transformations. Then, finger knuckle

Paper Details

Authors:
Gaurav Jaswal, Aditya Nigam, Ravinder Nath
Submitted On:
12 September 2017 - 10:58am
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2607_poster.pdf

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[1] Gaurav Jaswal, Aditya Nigam, Ravinder Nath, "DEFORMABLE MULTI-SCALE SCHEME FOR BIOMETRIC PERSONAL IDENTIFICATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1943. Accessed: Oct. 19, 2017.
@article{1943-17,
url = {http://sigport.org/1943},
author = {Gaurav Jaswal; Aditya Nigam; Ravinder Nath },
publisher = {IEEE SigPort},
title = {DEFORMABLE MULTI-SCALE SCHEME FOR BIOMETRIC PERSONAL IDENTIFICATION},
year = {2017} }
TY - EJOUR
T1 - DEFORMABLE MULTI-SCALE SCHEME FOR BIOMETRIC PERSONAL IDENTIFICATION
AU - Gaurav Jaswal; Aditya Nigam; Ravinder Nath
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1943
ER -
Gaurav Jaswal, Aditya Nigam, Ravinder Nath. (2017). DEFORMABLE MULTI-SCALE SCHEME FOR BIOMETRIC PERSONAL IDENTIFICATION. IEEE SigPort. http://sigport.org/1943
Gaurav Jaswal, Aditya Nigam, Ravinder Nath, 2017. DEFORMABLE MULTI-SCALE SCHEME FOR BIOMETRIC PERSONAL IDENTIFICATION. Available at: http://sigport.org/1943.
Gaurav Jaswal, Aditya Nigam, Ravinder Nath. (2017). "DEFORMABLE MULTI-SCALE SCHEME FOR BIOMETRIC PERSONAL IDENTIFICATION." Web.
1. Gaurav Jaswal, Aditya Nigam, Ravinder Nath. DEFORMABLE MULTI-SCALE SCHEME FOR BIOMETRIC PERSONAL IDENTIFICATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1943

Paper ID-2607 (Poster)


Human identification has now been a social liability due to
frequent terror threats and corrupt bureaucratic practices,
especially in rural countries like India. It has been surprisingly
observed that fingerprint quality is poor as compared
with finger knuckle quality of rural users as they exist on
the outer hand side. In this paper, we are proposing a novel
finger-knuckle-print based identification system. Initially,
finger knuckle image is preprocessed using proposed local
and adaptive image transformations. Then, finger knuckle

Paper Details

Authors:
Gaurav Jaswal, Aditya Nigam, Ravinder Nath
Submitted On:
12 September 2017 - 10:28am
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2607_poster.pdf

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[1] Gaurav Jaswal, Aditya Nigam, Ravinder Nath, "Paper ID-2607 (Poster)", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1938. Accessed: Oct. 19, 2017.
@article{1938-17,
url = {http://sigport.org/1938},
author = {Gaurav Jaswal; Aditya Nigam; Ravinder Nath },
publisher = {IEEE SigPort},
title = {Paper ID-2607 (Poster)},
year = {2017} }
TY - EJOUR
T1 - Paper ID-2607 (Poster)
AU - Gaurav Jaswal; Aditya Nigam; Ravinder Nath
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1938
ER -
Gaurav Jaswal, Aditya Nigam, Ravinder Nath. (2017). Paper ID-2607 (Poster). IEEE SigPort. http://sigport.org/1938
Gaurav Jaswal, Aditya Nigam, Ravinder Nath, 2017. Paper ID-2607 (Poster). Available at: http://sigport.org/1938.
Gaurav Jaswal, Aditya Nigam, Ravinder Nath. (2017). "Paper ID-2607 (Poster)." Web.
1. Gaurav Jaswal, Aditya Nigam, Ravinder Nath. Paper ID-2607 (Poster) [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1938

DCT BASED REGION LOG-TIEDRANK COVARIANCE MATRICES FOR FACE RECOGNITION

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Authors:
Submitted On:
20 March 2016 - 6:48am
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DCT-RLTRCM_ICASSP_Poster.pdf

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[1] , "DCT BASED REGION LOG-TIEDRANK COVARIANCE MATRICES FOR FACE RECOGNITION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/867. Accessed: Oct. 19, 2017.
@article{867-16,
url = {http://sigport.org/867},
author = { },
publisher = {IEEE SigPort},
title = {DCT BASED REGION LOG-TIEDRANK COVARIANCE MATRICES FOR FACE RECOGNITION},
year = {2016} }
TY - EJOUR
T1 - DCT BASED REGION LOG-TIEDRANK COVARIANCE MATRICES FOR FACE RECOGNITION
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/867
ER -
. (2016). DCT BASED REGION LOG-TIEDRANK COVARIANCE MATRICES FOR FACE RECOGNITION. IEEE SigPort. http://sigport.org/867
, 2016. DCT BASED REGION LOG-TIEDRANK COVARIANCE MATRICES FOR FACE RECOGNITION. Available at: http://sigport.org/867.
. (2016). "DCT BASED REGION LOG-TIEDRANK COVARIANCE MATRICES FOR FACE RECOGNITION." Web.
1. . DCT BASED REGION LOG-TIEDRANK COVARIANCE MATRICES FOR FACE RECOGNITION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/867

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.

Paper Details

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: Oct. 19, 2017.
@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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