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Biometrics

IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS


Presentation attack detection (PAD) is now considered critically important for any face-recognition (FR) based access-control system. Current deep-learning based PAD systems show excellent performance when they are tested in intra-dataset scenarios. Under cross-dataset evaluation the performance of these PAD systems drops significantly. This lack of generalization is attributed to domain-shift. Here, we propose a novel PAD method that leverages the large variability present in FR datasets to induce invariance to factors that cause domain-shift.

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Authors:
Sushil Bhattacharjee, Sebastien Marcel
Submitted On:
15 May 2020 - 10:22am
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[1] Sushil Bhattacharjee, Sebastien Marcel, "IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5350. Accessed: Jun. 07, 2020.
@article{5350-20,
url = {http://sigport.org/5350},
author = {Sushil Bhattacharjee; Sebastien Marcel },
publisher = {IEEE SigPort},
title = {IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS},
year = {2020} }
TY - EJOUR
T1 - IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS
AU - Sushil Bhattacharjee; Sebastien Marcel
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5350
ER -
Sushil Bhattacharjee, Sebastien Marcel. (2020). IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS. IEEE SigPort. http://sigport.org/5350
Sushil Bhattacharjee, Sebastien Marcel, 2020. IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS. Available at: http://sigport.org/5350.
Sushil Bhattacharjee, Sebastien Marcel. (2020). "IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS." Web.
1. Sushil Bhattacharjee, Sebastien Marcel. IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5350

DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION


With face-recognition (FR) increasingly replacing fingerprint sensors for user-authentication on mobile devices, presentation attacks (PA) have emerged as the single most significant hurdle for manufacturers of FR systems. Current machine-learning based presentation attack detection (PAD) systems, trained in a data-driven fashion, show excellent performance when evaluated in intra-dataset scenarios. Their performance typically degrades significantly in cross-dataset evaluations. This lack of generalization in current PAD systems makes them unsuitable for deployment in real-world scenarios.

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Authors:
Sushil Bhattacharjee, Sebastien Marcel
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15 May 2020 - 10:19am
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[1] Sushil Bhattacharjee, Sebastien Marcel, "DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5349. Accessed: Jun. 07, 2020.
@article{5349-20,
url = {http://sigport.org/5349},
author = {Sushil Bhattacharjee; Sebastien Marcel },
publisher = {IEEE SigPort},
title = {DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION},
year = {2020} }
TY - EJOUR
T1 - DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION
AU - Sushil Bhattacharjee; Sebastien Marcel
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5349
ER -
Sushil Bhattacharjee, Sebastien Marcel. (2020). DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION. IEEE SigPort. http://sigport.org/5349
Sushil Bhattacharjee, Sebastien Marcel, 2020. DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION. Available at: http://sigport.org/5349.
Sushil Bhattacharjee, Sebastien Marcel. (2020). "DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION." Web.
1. Sushil Bhattacharjee, Sebastien Marcel. DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5349

Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction


Latent fingerprint reconstruction is a vital preprocessing step for its identification. This task is very challenging due to not only existing complicated degradation patterns but also its scarcity of paired training data. To address these challenges, we propose a novel generative adversarial network (GAN) based data augmentation scheme to improve such reconstruction.

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Authors:
Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang
Submitted On:
15 May 2020 - 1:16am
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ICASSP1263.pdf

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[1] Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang, "Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5335. Accessed: Jun. 07, 2020.
@article{5335-20,
url = {http://sigport.org/5335},
author = {Ying Xu; Yi Wang; Jiajun Liang; Yong Jiang },
publisher = {IEEE SigPort},
title = {Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction},
year = {2020} }
TY - EJOUR
T1 - Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction
AU - Ying Xu; Yi Wang; Jiajun Liang; Yong Jiang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5335
ER -
Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang. (2020). Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction. IEEE SigPort. http://sigport.org/5335
Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang, 2020. Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction. Available at: http://sigport.org/5335.
Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang. (2020). "Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction." Web.
1. Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang. Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5335

A LIGHTWEIGHT MULTI-LABEL SEGMENTATION NETWORK FOR MOBILE IRIS BIOMETRICS

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Authors:
Caiyong Wang, Yunlong Wang, Boqiang Xu, Yong He, Zhiwei Dong, Zhenan Sun
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13 May 2020 - 10:00pm
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[1] Caiyong Wang, Yunlong Wang, Boqiang Xu, Yong He, Zhiwei Dong, Zhenan Sun, "A LIGHTWEIGHT MULTI-LABEL SEGMENTATION NETWORK FOR MOBILE IRIS BIOMETRICS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5189. Accessed: Jun. 07, 2020.
@article{5189-20,
url = {http://sigport.org/5189},
author = {Caiyong Wang; Yunlong Wang; Boqiang Xu; Yong He; Zhiwei Dong; Zhenan Sun },
publisher = {IEEE SigPort},
title = {A LIGHTWEIGHT MULTI-LABEL SEGMENTATION NETWORK FOR MOBILE IRIS BIOMETRICS},
year = {2020} }
TY - EJOUR
T1 - A LIGHTWEIGHT MULTI-LABEL SEGMENTATION NETWORK FOR MOBILE IRIS BIOMETRICS
AU - Caiyong Wang; Yunlong Wang; Boqiang Xu; Yong He; Zhiwei Dong; Zhenan Sun
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5189
ER -
Caiyong Wang, Yunlong Wang, Boqiang Xu, Yong He, Zhiwei Dong, Zhenan Sun. (2020). A LIGHTWEIGHT MULTI-LABEL SEGMENTATION NETWORK FOR MOBILE IRIS BIOMETRICS. IEEE SigPort. http://sigport.org/5189
Caiyong Wang, Yunlong Wang, Boqiang Xu, Yong He, Zhiwei Dong, Zhenan Sun, 2020. A LIGHTWEIGHT MULTI-LABEL SEGMENTATION NETWORK FOR MOBILE IRIS BIOMETRICS. Available at: http://sigport.org/5189.
Caiyong Wang, Yunlong Wang, Boqiang Xu, Yong He, Zhiwei Dong, Zhenan Sun. (2020). "A LIGHTWEIGHT MULTI-LABEL SEGMENTATION NETWORK FOR MOBILE IRIS BIOMETRICS." Web.
1. Caiyong Wang, Yunlong Wang, Boqiang Xu, Yong He, Zhiwei Dong, Zhenan Sun. A LIGHTWEIGHT MULTI-LABEL SEGMENTATION NETWORK FOR MOBILE IRIS BIOMETRICS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5189

Low-complexity and Reliable Transforms for Physical Unclonable Functions


Noisy measurements of a physical unclonable function (PUF) are used to store secret keys with reliability, security, privacy, and complexity constraints. A new set of low-complexity and orthogonal transforms with no multiplication is proposed to obtain bit-error probability results significantly better than all methods previously proposed for key binding with PUFs. The uniqueness and security performance of a transform selected from the proposed set is shown to be close to optimal.

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Authors:
Onur Günlü and Rafael F. Schaefer
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5 May 2020 - 11:06am
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[1] Onur Günlü and Rafael F. Schaefer, "Low-complexity and Reliable Transforms for Physical Unclonable Functions", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5124. Accessed: Jun. 07, 2020.
@article{5124-20,
url = {http://sigport.org/5124},
author = {Onur Günlü and Rafael F. Schaefer },
publisher = {IEEE SigPort},
title = {Low-complexity and Reliable Transforms for Physical Unclonable Functions},
year = {2020} }
TY - EJOUR
T1 - Low-complexity and Reliable Transforms for Physical Unclonable Functions
AU - Onur Günlü and Rafael F. Schaefer
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5124
ER -
Onur Günlü and Rafael F. Schaefer. (2020). Low-complexity and Reliable Transforms for Physical Unclonable Functions. IEEE SigPort. http://sigport.org/5124
Onur Günlü and Rafael F. Schaefer, 2020. Low-complexity and Reliable Transforms for Physical Unclonable Functions. Available at: http://sigport.org/5124.
Onur Günlü and Rafael F. Schaefer. (2020). "Low-complexity and Reliable Transforms for Physical Unclonable Functions." Web.
1. Onur Günlü and Rafael F. Schaefer. Low-complexity and Reliable Transforms for Physical Unclonable Functions [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5124

DuoDepth: Static Gesture Recognition with Dual Depth Sensors


Static gesture recognition is an effective non-verbal communication channel between a user and their devices; however many modern methods are sensitive to the relative pose of the user’s hands with respect to the capture device, as parts of the gesture can become occluded. We present two methodologies for gesture recognition via synchronized recording from two depth cameras to alleviate this occlusion problem.

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Authors:
Ilya Chugunov, Avideh Zakhor
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21 September 2019 - 2:36am
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DuoDepth ICIP 2019 Poster

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[1] Ilya Chugunov, Avideh Zakhor, "DuoDepth: Static Gesture Recognition with Dual Depth Sensors", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4801. Accessed: Jun. 07, 2020.
@article{4801-19,
url = {http://sigport.org/4801},
author = {Ilya Chugunov; Avideh Zakhor },
publisher = {IEEE SigPort},
title = {DuoDepth: Static Gesture Recognition with Dual Depth Sensors},
year = {2019} }
TY - EJOUR
T1 - DuoDepth: Static Gesture Recognition with Dual Depth Sensors
AU - Ilya Chugunov; Avideh Zakhor
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4801
ER -
Ilya Chugunov, Avideh Zakhor. (2019). DuoDepth: Static Gesture Recognition with Dual Depth Sensors. IEEE SigPort. http://sigport.org/4801
Ilya Chugunov, Avideh Zakhor, 2019. DuoDepth: Static Gesture Recognition with Dual Depth Sensors. Available at: http://sigport.org/4801.
Ilya Chugunov, Avideh Zakhor. (2019). "DuoDepth: Static Gesture Recognition with Dual Depth Sensors." Web.
1. Ilya Chugunov, Avideh Zakhor. DuoDepth: Static Gesture Recognition with Dual Depth Sensors [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4801

DYNAMIC FACIAL FEATURES FOR INHERENTLY SAFER FACE RECOGNITION


Among the many known type of intra-class variations, facial expressions are considered particularly challenging, as witnessed by the large number of methods that have been proposed to cope with them. The idea inspiring this work is that dynamic facial features (DFF) extracted from facial expressions while a sentence is pronounced, could possibly represent a salient and inherently safer biometric identifier, due to the greater difficulty in forging a time variable descriptor instead of a static one.

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Authors:
Davide Iengo, Michele Nappi, Davide Vanore
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18 September 2019 - 7:03pm
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Poster_Presentation_Paper #3041.pdf

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[1] Davide Iengo, Michele Nappi, Davide Vanore, "DYNAMIC FACIAL FEATURES FOR INHERENTLY SAFER FACE RECOGNITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4669. Accessed: Jun. 07, 2020.
@article{4669-19,
url = {http://sigport.org/4669},
author = {Davide Iengo; Michele Nappi; Davide Vanore },
publisher = {IEEE SigPort},
title = {DYNAMIC FACIAL FEATURES FOR INHERENTLY SAFER FACE RECOGNITION},
year = {2019} }
TY - EJOUR
T1 - DYNAMIC FACIAL FEATURES FOR INHERENTLY SAFER FACE RECOGNITION
AU - Davide Iengo; Michele Nappi; Davide Vanore
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4669
ER -
Davide Iengo, Michele Nappi, Davide Vanore. (2019). DYNAMIC FACIAL FEATURES FOR INHERENTLY SAFER FACE RECOGNITION. IEEE SigPort. http://sigport.org/4669
Davide Iengo, Michele Nappi, Davide Vanore, 2019. DYNAMIC FACIAL FEATURES FOR INHERENTLY SAFER FACE RECOGNITION. Available at: http://sigport.org/4669.
Davide Iengo, Michele Nappi, Davide Vanore. (2019). "DYNAMIC FACIAL FEATURES FOR INHERENTLY SAFER FACE RECOGNITION." Web.
1. Davide Iengo, Michele Nappi, Davide Vanore. DYNAMIC FACIAL FEATURES FOR INHERENTLY SAFER FACE RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4669

A CASCADED NOISE-ROBUST DEEP CNN FOR FACE RECOGNITION


State-of-the-art face recognition methods have achieved ex- cellent performance on the clean datasets. However, in real- world applications, the captured face images are usually contaminated with noise, which significantly decreases the performance of these face recognition methods. In this pa- per, we propose a cascaded noise-robust deep convolutional neural network (CNR-CNN) method, consisting of two sub- networks, i.e., a denoising sub-network and a face recognition sub-network, for face recognition under noise.

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Authors:
Xiangbang Meng,Yan Yan,Si Chen, Hanzi Wang
Submitted On:
10 September 2019 - 10:52pm
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A CASCADED NOISE-ROBUST DEEP CNN FOR FACE RECOGNITION.pdf

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[1] Xiangbang Meng,Yan Yan,Si Chen, Hanzi Wang, "A CASCADED NOISE-ROBUST DEEP CNN FOR FACE RECOGNITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4584. Accessed: Jun. 07, 2020.
@article{4584-19,
url = {http://sigport.org/4584},
author = {Xiangbang Meng;Yan Yan;Si Chen; Hanzi Wang },
publisher = {IEEE SigPort},
title = {A CASCADED NOISE-ROBUST DEEP CNN FOR FACE RECOGNITION},
year = {2019} }
TY - EJOUR
T1 - A CASCADED NOISE-ROBUST DEEP CNN FOR FACE RECOGNITION
AU - Xiangbang Meng;Yan Yan;Si Chen; Hanzi Wang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4584
ER -
Xiangbang Meng,Yan Yan,Si Chen, Hanzi Wang. (2019). A CASCADED NOISE-ROBUST DEEP CNN FOR FACE RECOGNITION. IEEE SigPort. http://sigport.org/4584
Xiangbang Meng,Yan Yan,Si Chen, Hanzi Wang, 2019. A CASCADED NOISE-ROBUST DEEP CNN FOR FACE RECOGNITION. Available at: http://sigport.org/4584.
Xiangbang Meng,Yan Yan,Si Chen, Hanzi Wang. (2019). "A CASCADED NOISE-ROBUST DEEP CNN FOR FACE RECOGNITION." Web.
1. Xiangbang Meng,Yan Yan,Si Chen, Hanzi Wang. A CASCADED NOISE-ROBUST DEEP CNN FOR FACE RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4584

Securing smartphone handwritten PIN codes with recurrent neural networks

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Authors:
Gaël LE LAN, Vincent FREY
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15 May 2019 - 6:21am
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[1] Gaël LE LAN, Vincent FREY, "Securing smartphone handwritten PIN codes with recurrent neural networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4521. Accessed: Jun. 07, 2020.
@article{4521-19,
url = {http://sigport.org/4521},
author = {Gaël LE LAN; Vincent FREY },
publisher = {IEEE SigPort},
title = {Securing smartphone handwritten PIN codes with recurrent neural networks},
year = {2019} }
TY - EJOUR
T1 - Securing smartphone handwritten PIN codes with recurrent neural networks
AU - Gaël LE LAN; Vincent FREY
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4521
ER -
Gaël LE LAN, Vincent FREY. (2019). Securing smartphone handwritten PIN codes with recurrent neural networks. IEEE SigPort. http://sigport.org/4521
Gaël LE LAN, Vincent FREY, 2019. Securing smartphone handwritten PIN codes with recurrent neural networks. Available at: http://sigport.org/4521.
Gaël LE LAN, Vincent FREY. (2019). "Securing smartphone handwritten PIN codes with recurrent neural networks." Web.
1. Gaël LE LAN, Vincent FREY. Securing smartphone handwritten PIN codes with recurrent neural networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4521

AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION


This paper proposes a group membership verification protocol preventing the curious but honest server from reconstructing the enrolled signatures and inferring the identity of querying clients. The protocol quantizes the signatures into discrete embeddings, making reconstruction difficult. It also aggregates multiple embeddings into representative values, impeding identification. Theoretical and experimental results show the trade-off between the security and error rates.

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Authors:
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy
Submitted On:
13 May 2019 - 9:23am
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conference_poster_4.pdf

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[1] Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy, "AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4486. Accessed: Jun. 07, 2020.
@article{4486-19,
url = {http://sigport.org/4486},
author = {Marzieh Gheisari; Teddy Furon; Laurent Amsaleg; Behrooz Razeghi; Slava Voloshynovskiy },
publisher = {IEEE SigPort},
title = {AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION},
year = {2019} }
TY - EJOUR
T1 - AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION
AU - Marzieh Gheisari; Teddy Furon; Laurent Amsaleg; Behrooz Razeghi; Slava Voloshynovskiy
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4486
ER -
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. (2019). AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION. IEEE SigPort. http://sigport.org/4486
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy, 2019. AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION. Available at: http://sigport.org/4486.
Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. (2019). "AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION." Web.
1. Marzieh Gheisari, Teddy Furon, Laurent Amsaleg, Behrooz Razeghi, Slava Voloshynovskiy. AGGREGATION AND EMBEDDING FOR GROUP MEMBERSHIP VERIFICATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4486

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