scholarly journals Thresholded Two-Phase Test Sample Representation for Outlier Rejection in Biological Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Xiang Wu ◽  
Ning Wu
Keyword(s):  
Face Recognition ◽  
Object Recognition ◽  
Test Sample ◽  
Complex Object ◽  
Outlier Rejection ◽  
Two Phase ◽  
Real World Applications ◽  
Biological Recognition ◽  
Global Representation ◽  
Sample Representation

The two-phase test sample representation (TPTSR) was proposed as a useful classifier for face recognition. However, the TPTSR method is not able to reject the impostor, so it should be modified for real-world applications. This paper introduces a thresholded TPTSR (T-TPTSR) method for complex object recognition with outliers, and two criteria for assessing the performance of outlier rejection and member classification are defined. The performance of the T-TPTSR method is compared with the modified global representation, PCA and LDA methods, respectively. The results show that the T-TPTSR method achieves the best performance among them according to the two criteria.

A Kernel Two-Phase Test Sample Sparse Representation for Face Recognition

2015 ◽  
Vol 30 (01) ◽  
pp. 1656001
Author(s):  
Zhonghua Liu ◽  
Jiexin Pu ◽  
Yong Qiu ◽  
Moli Zhang ◽  
Xiaoli Zhang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Recognition Performance ◽  
Mapping Function ◽  
Feature Space ◽  
Test Sample ◽  
Training Data ◽  
Two Phase ◽  
Kernel Parameter ◽  
Representation Method

Sparse representation is a new hot technique in recent years. The two-phase test sample sparse representation method (TPTSSR) achieved an excellent performance in face recognition. In this paper, a kernel two-phase test sample sparse representation method (KTPTSSR) is proposed. Firstly, the input data are mapped into an implicit high-dimensional feature space by a nonlinear mapping function. Secondly, the data are analyzed by means of the TPTSSR method in the feature space. If an appropriate kernel function and the corresponding kernel parameter are selected, a test sample can be accurately represented as the linear combination of the training data with the same label information of the test sample. Therefore, the proposed method could have better recognition performance than TPTSSR. Experiments on the face databases demonstrate the effectiveness of our methods.

scholarly journals A Collaborative Neighbor Representation Based Face Recognition Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Zhengming Li ◽  
Qi Zhu ◽  
Binglei Xie ◽  
Jian Cao ◽  
Jin Zhang
Keyword(s):  
Face Recognition ◽  
Linear Combination ◽  
Test Sample ◽  
Reconstruction Error ◽  
Recognition Algorithm ◽  
Second Phase ◽  
Two Phase ◽  
Alternative Scheme ◽  
Training Samples ◽  
Discrimination Information

We propose a new collaborative neighbor representation algorithm for face recognition based on a revised regularized reconstruction error (RRRE), called the two-phase collaborative neighbor representation algorithm (TCNR). Specifically, the RRRE is the division of  l2-norm of reconstruction error of each class into a linear combination of  l2-norm of reconstruction coefficients of each class, which can be used to increase the discrimination information for classification. The algorithm is as follows: in the first phase, the test sample is represented as a linear combination of all the training samples by incorporating the neighbor information into the objective function. In the second phase, we use thekclasses to represent the test sample and calculate the collaborative neighbor representation coefficients. TCNR not only can preserve locality and similarity information of sparse coding but also can eliminate the side effect on the classification decision of the class that is far from the test sample. Moreover, the rationale and alternative scheme of TCNR are given. The experimental results show that TCNR algorithm achieves better performance than seven previous algorithms.

scholarly journals A Novel Face Recognition Approach Based on Two-Step Test Sample Representation

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Zun-xiong Liu ◽  
Zhi-qiang Huang ◽  
Heng Zhang
Keyword(s):  
Face Recognition ◽  
Side Effect ◽  
Test Sample ◽  
The Other ◽  
Step Test ◽  
Weighted Sum ◽  
Training Samples ◽  
Representation Method ◽  
Sample Representation ◽  
Representative Samples

The two-step test sample representation method is proposed for face recognition. It first identifiesk“representative” samples from each category training samples for the test sample then produces a weighted sum of all the “representative” samples that well approximates the test sample. This method assigns the test sample to the class whose training samples have the smallest deviation from the test sample. As the method proposed in this paper is able to reduce the side-effect of the other training samples that are very “far” from the test sample on the recognition decision of the test sample, the high recognition rates can be obtained.

Face Recognition Via Weighted Two Phase Test Sample Sparse Representation

2013 ◽  
Vol 41 (1) ◽  
pp. 43-53 ◽  
Author(s):  
Zhonghua Liu ◽  
Jiexin Pu ◽  
Meiyu Xu ◽  
Yong Qiu
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Test Sample ◽  
Two Phase

Face Perception in Face Transplant Patients

2019 ◽  
Vol 35 (05) ◽  
pp. 525-533
Author(s):  
Evrim Gülbetekin ◽  
Seda Bayraktar ◽  
Özlenen Özkan ◽  
Hilmi Uysal ◽  
Ömer Özkan
Keyword(s):  
Face Recognition ◽  
Object Recognition ◽  
Face Processing ◽  
Recognition Performance ◽  
Discrimination Performance ◽  
Object Discrimination ◽  
Face Transplantation ◽  
Transplant Patients ◽  
Facial Surgery ◽  
The Face

AbstractThe authors tested face discrimination, face recognition, object discrimination, and object recognition in two face transplantation patients (FTPs) who had facial injury since infancy, a patient who had a facial surgery due to a recent wound, and two control subjects. In Experiment 1, the authors showed them original faces and morphed forms of those faces and asked them to rate the similarity between the two. In Experiment 2, they showed old, new, and implicit faces and asked whether they recognized them or not. In Experiment 3, they showed them original objects and morphed forms of those objects and asked them to rate the similarity between the two. In Experiment 4, they showed old, new, and implicit objects and asked whether they recognized them or not. Object discrimination and object recognition performance did not differ between the FTPs and the controls. However, the face discrimination performance of FTP2 and face recognition performance of the FTP1 were poorer than that of the controls were. Therefore, the authors concluded that the structure of the face might affect face processing.

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