FaceLiveNet: End-to-End Networks Combining Face Verification with Interactive Facial Expression-Based Liveness Detection

2018 ◽  
Author(s):  
Zuheng Ming ◽  
Joseph Chazalon ◽  
Muhammad Muzzamil Luqman ◽  
Muriel Visani ◽  
Jean-Christophe Burie
Keyword(s):  
Facial Expression ◽  
Face Verification ◽  
Liveness Detection ◽  
End To End

Joint Bayesian guided metric learning for end-to-end face verification

2018 ◽  
Vol 275 ◽  
pp. 560-567 ◽  
Author(s):  
Di Chen ◽  
Chunyan Xu ◽  
Jian Yang ◽  
Jianjun Qian ◽  
Yuhui Zheng ◽  
...  
Keyword(s):  
Metric Learning ◽  
Face Verification ◽  
End To End

scholarly journals Attended End-to-End Architecture for Age Estimation From Facial Expression Videos

2020 ◽  
Vol 29 ◽  
pp. 1972-1984 ◽  
Author(s):  
Wenjie Pei ◽  
Hamdi Dibeklioglu ◽  
Tadas Baltrusaitis ◽  
David M. J. Tax
Keyword(s):  
Facial Expression ◽  
Age Estimation ◽  
End To End

scholarly journals Liveness Detection Based on Human eye Blinking for Photo Attacks

2019 ◽  
Vol 9 (1) ◽  
pp. 4074-4077
Keyword(s):  
Face Recognition ◽  
Aspect Ratio ◽  
Face Verification ◽  
Human Face ◽  
Eye Blink ◽  
Liveness Detection ◽  
Small Window ◽  
Time Period ◽  
Accuracy Result ◽  
Security Check

Auto face recognition mainly implemented to avoid the replication of identity to demonstrate through security check. This rage of face verification has brought intensive interest about facial biometric towards attacks of spoofing, in which a person’s mask or photo can be produced to be authorized. So, we propose a liveness detection based on eye blinking, where eyes are extracted from human face. The method of face recognition was applied by utilizing OpenCV classifier and dlib library, and a concept of edge detection and calculation of structure to extract the portion of the eye and to observe and make note of variation in the attributes of the eyes over a time period was employed. The landmarks are plotted accurately enough to derive the state of eye if it is closed or opened. A scalar quantity EAR (eye aspect ratio) is derived from landmark positions defined by the algorithm to identify a blink corresponding to every frame. The set of EAR values of successive frames are detected as a eye blink by a OpenCV classifier displayed on a small window when person is in front of camera. Finally, it gives the accuracy result whether it is human being or spoof attack.

scholarly journals Live Face Verification with Multiple Instantialized Local Homographic Parameterization

2018 ◽  
Author(s):  
Chen Lin ◽  
Zhouyingcheng Liao ◽  
Peng Zhou ◽  
Jianguo Hu ◽  
Bingbing Ni
Keyword(s):  
Facial Expression ◽  
Recall Rate ◽  
Multiple Instance Learning ◽  
Superior Performance ◽  
Face Verification ◽  
Facial Movements ◽  
Learning Framework ◽  
Verification Methods ◽  
Movement Sequence ◽  
Motion Parameterization

State-of-the-art live face verification methods would easily be attacked by recorded facial expression sequence. This work directly addresses this issue via proposing a patch-wise motion parameterization based verification network infrastructure. This method directly explores the underlying subtle motion difference between the facial movements re-captured from a planer screen (e.g., a pad) and those from a real face; therefore interactive facial expression is no longer required. Furthermore, inspired by the fact that ?a fake facial movement sequence MUST contains many patch-wise fake sequences?, we embed our network into a multiple instance learning framework, which further enhance the recall rate of the proposed technique. Extensive experimental results on several face benchmarks well demonstrate the superior performance of our method.

scholarly journals Distillation of an End-to-End Oracle for Face Verification and Recognition Sensors

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1369
Author(s):  
Francesco Guzzi ◽  
Luca De Bortoli ◽  
Romina Soledad Molina ◽  
Stefano Marsi ◽  
Sergio Carrato ◽  
...  
Keyword(s):  
Face Recognition ◽  
Complex Model ◽  
Face Verification ◽  
Security Systems ◽  
Alignment Procedure ◽  
Open Set ◽  
Minimal Structure ◽  
Sensor Module ◽  
Network Methods ◽  
End To End

Face recognition functions are today exploited through biometric sensors in many applications, from extended security systems to inclusion devices; deep neural network methods are reaching in this field stunning performances. The main limitation of the deep learning approach is an inconvenient relation between the accuracy of the results and the needed computing power. When a personal device is employed, in particular, many algorithms require a cloud computing approach to achieve the expected performances; other algorithms adopt models that are simple by design. A third viable option consists of model (oracle) distillation. This is the most intriguing among the compression techniques since it permits to devise of the minimal structure that will enforce the same I/O relation as the original model. In this paper, a distillation technique is applied to a complex model, enabling the introduction of fast state-of-the-art recognition capabilities on a low-end hardware face recognition sensor module. Two distilled models are presented in this contribution: the former can be directly used in place of the original oracle, while the latter incarnates better the end-to-end approach, removing the need for a separate alignment procedure. The presented biometric systems are examined on the two problems of face verification and face recognition in an open set by using well-agreed training/testing methodologies and datasets.

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