scholarly journals Open set face recognition using transduction

2005 ◽  
Vol 27 (11) ◽  
pp. 1686-1697 ◽  
Author(s):  
Fayin Li ◽  
H. Wechsler
Keyword(s):  
Face Recognition ◽  
Open Set

scholarly journals Open-set Face Recognition for Small Galleries Using Siamese Networks

2020 ◽  
Author(s):  
Gabriel Salomon ◽  
Alceu Britto ◽  
Rafael H. Vareto ◽  
William R. Schwartz ◽  
David Menotti
Keyword(s):  
Face Recognition ◽  
Open Set ◽  
Siamese Networks

Low-resolution and open-set face recognition via recursive label propagation based on statistical classification

2019 ◽  
Vol 17 (02) ◽  
pp. 1940002 ◽  
Author(s):  
Shan Xue ◽  
Hong Zhu
Keyword(s):  
Face Recognition ◽  
Super Resolution ◽  
Recall Rate ◽  
Face Image ◽  
Label Propagation ◽  
Statistical Classification ◽  
Low Resolution ◽  
Face Images ◽  
Open Set ◽  
Discriminative Feature

In video surveillance, the captured face images are usually suffered from low-resolution (LR), besides, not all the probe images have mates in the gallery under the premise that only a single frontal high-resolution (HR) face image per subject. To address this problem, a novel face recognition framework called recursive label propagation based on statistical classification (ReLPBSC) has been proposed in this paper. Firstly, we employ VGG to extract robust discriminative feature vectors to represent each face. Then we select the corresponding LR face in the probe for each HR gallery face by similarity. Based on the picked HR–LR pairs, ReLPBSC is implemented for recognition. The main contributions of the proposed approach are as follows: (i) Inspired by substantial achievements of deep learning methods, VGG is adopted to achieve discriminative representation for LR faces to avoid the super-resolution steps; (ii) the accepted and rejected threshold parameters, which are not fixed in face recognition, can be achieved with ReLPBSC adaptively; (iii) the unreliable subjects never enrolled in the gallery can be rejected automatically with designed methods. Experimental results in [Formula: see text] pixels resolution show that the proposed method can achieve 86.64% recall rate while keeping 100% precision.

Open set face recognition with deep transfer learning and extreme value statistics

2018 ◽  
Vol 16 (04) ◽  
pp. 1850034 ◽  
Author(s):  
Hao Xie ◽  
Yunyan Du ◽  
Huapeng Yu ◽  
Yongxin Chang ◽  
Zhiyong Xu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Standard Deviation ◽  
Transfer Learning ◽  
Lower Layer ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Distribution Fitting ◽  
Open Set ◽  
Deep Model ◽  
Novel Approach

Deep face recognition model learned on big dataset surpasses humans on difficult unconstrained face dataset. But open set face recognition, i.e. robust to both variations and unknown faces, is still a big challenge. In this paper, we propose a robust open set face recognition approach with deep transfer learning and extreme value statistics. First, we demonstrate that transferring the feature representations of a pre-trained deep face model to specific tasks is an efficient and effective approach for face recognition on small datasets. We learn both higher layer representations and the final linear multi-class SVMs with transferred features. Second, we propose a novel approach for unknown people recognition with extreme value statistics. Different from traditional distribution fitting, our approach only makes use of a simple statistical quantity — standard deviation of tail data. Empirical evidence shows that standard deviation of the tail of multi-class SVMs recognition scores is efficient and robust for unknown people recognition. Finally, we also empirically explore an important open problem — attributes and transferability of different layer features of the deep model. We argue that lower layer features are both local and general, while higher layer ones are both global and specific which embrace both intra-class invariance and inter-class discrimination. The results of unsupervised feature visualization and supervised face identification strongly support our view.

Open-set face recognition across look-alike faces in real-world scenarios

2017 ◽  
Vol 57 ◽  
pp. 1-14 ◽  
Author(s):  
Ali Moeini ◽  
Karim Faez ◽  
Hossein Moeini ◽  
Armon Matthew Safai
Keyword(s):  
Face Recognition ◽  
Real World ◽  
Open Set

scholarly journals Multi-modal Open World User Identification

2022 ◽  
Vol 11 (1) ◽  
pp. 1-50
Author(s):  
Bahar Irfan ◽  
Michael Garcia Ortiz ◽  
Natalia Lyubova ◽  
Tony Belpaeme
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Human Robot Interaction ◽  
User Identification ◽  
Closed Set ◽  
Identification Rate ◽  
Open World ◽  
Open Set ◽  
User Recognition

User identification is an essential step in creating a personalised long-term interaction with robots. This requires learning the users continuously and incrementally, possibly starting from a state without any known user. In this article, we describe a multi-modal incremental Bayesian network with online learning, which is the first method that can be applied in such scenarios. Face recognition is used as the primary biometric, and it is combined with ancillary information, such as gender, age, height, and time of interaction to improve the recognition. The Multi-modal Long-term User Recognition Dataset is generated to simulate various human-robot interaction (HRI) scenarios and evaluate our approach in comparison to face recognition, soft biometrics, and a state-of-the-art open world recognition method (Extreme Value Machine). The results show that the proposed methods significantly outperform the baselines, with an increase in the identification rate up to 47.9% in open-set and closed-set scenarios, and a significant decrease in long-term recognition performance loss. The proposed models generalise well to new users, provide stability, improve over time, and decrease the bias of face recognition. The models were applied in HRI studies for user recognition, personalised rehabilitation, and customer-oriented service, which showed that they are suitable for long-term HRI in the real world.

Sign in / Sign up

Export Citation Format

Share Document