scholarly journals Patch Based Collaborative Representation with Gabor Feature and Measurement Matrix for Face Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Zhengyuan Xu ◽  
Yu Liu ◽  
Mingquan Ye ◽  
Lei Huang ◽  
Hao Yu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Sample Size ◽  
Small Sample Size ◽  
Large Data ◽  
Linear Representation ◽  
Small Sample ◽  
Collaborative Representation ◽  
Measurement Matrix ◽  
Gabor Feature ◽  
The Impact

In recent years, sparse representation based classification (SRC) has emerged as a popular technique in face recognition. Traditional SRC focuses on the role of the l1-norm but ignores the impact of collaborative representation (CR), which employs all the training examples over all the classes to represent a test sample. Due to issues like expression, illumination, pose, and small sample size, face recognition still remains as a challenging problem. In this paper, we proposed a patch based collaborative representation method for face recognition via Gabor feature and measurement matrix. Using patch based collaborative representation, this method can solve the problem of the lack of accuracy for the linear representation of the small sample size. Compared with holistic features, the multiscale and multidirection Gabor feature shows more robustness. The usage of measurement matrix can reduce large data volume caused by Gabor feature. The experimental results on several popular face databases including Extended Yale B, CMU_PIE, and LFW indicated that the proposed method is more competitive in robustness and accuracy than conventional SR and CR based methods.

Kernel Null Space Marginal Fisher Analysis for Face Recognition

2014 ◽  
Vol 889-890 ◽  
pp. 1065-1068
Author(s):  
Yu’e Lin ◽  
Xing Zhu Liang ◽  
Hua Ping Zhou
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Sample Size ◽  
High Performance ◽  
Null Space ◽  
Small Sample Size ◽  
Recognition Rate ◽  
Original Data ◽  
Small Sample ◽  
Neighborhood Structure

In the recent years, the feature extraction algorithms based on manifold learning, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure, have drawn much attention. Among them, the Marginal Fisher Analysis (MFA) achieved high performance for face recognition. However, MFA suffers from the small sample size problems and is still a linear technique. This paper develops a new nonlinear feature extraction algorithm, called Kernel Null Space Marginal Fisher Analysis (KNSMFA). KNSMFA based on a new optimization criterion is presented, which means that all the discriminant vectors can be calculated in the null space of the within-class scatter. KNSMFA not only exploits the nonlinear features but also overcomes the small sample size problems. Experimental results on ORL database indicate that the proposed method achieves higher recognition rate than the MFA method and some existing kernel feature extraction algorithms.

scholarly journals The impact of visual impairment on nutritional status: A systematic review

2018 ◽  
Vol 36 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Nabila Jones ◽  
Hannah Bartlett
Keyword(s):  
Nutritional Status ◽  
Visual Impairment ◽  
Sample Size ◽  
Visually Impaired ◽  
Small Sample Size ◽  
Small Sample ◽  
Control Group ◽  
Visually Impaired People ◽  
Impaired People ◽  
The Impact

The aim of this review was to evaluate the literature that has investigated the impact of visual impairment on nutritional status. We identified relevant articles through a multi-staged systematic approach. Fourteen articles were identified as meeting the inclusion criteria. The sample size of the studies ranged from 9 to 761 participants. It was found that visual impairment significantly affects nutritional status. The studies reported that visually impaired people have an abnormal body mass index (BMI); a higher prevalence of obesity and malnutrition was reported. Visually impaired people find it difficult to shop for, eat, and prepare meals. Most studies had a small sample size, and some studies did not include a study control group for comparison. The limitations of these studies suggest that the findings are not conclusive enough to hold true for only those who are visually impaired. Further studies with a larger sample size are required with the aim of developing interventions.

Role of an extended pathology exam in risk classification of testicular germ cell tumors (GCTs).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e15032-e15032
Author(s):  
Mihai Vasile Marinca ◽  
Irina Draga Caruntu ◽  
Ludmila Liliac ◽  
Simona Eliza Giusca ◽  
Andreea Marinca ◽  
...  
Keyword(s):  
Sample Size ◽  
Small Sample Size ◽  
Germ Cell Tumors ◽  
Disease Free Survival ◽  
Developed Countries ◽  
Small Sample ◽  
P Value ◽  
Testicular Germ Cell ◽  
Therapeutic Decisions ◽  
The Impact

e15032 Background: The 1997 IGCCCG Consensus classification provides clinicians with enough information to efficiently choose between treatment options for most GCT patients. Nevertheless, therapy is ineffective in 5-10% of cases (even more in less developed countries), and about the same numbers experience severe side effects. This exploratory study aims to assess the impact of more rigorous and detailed pathology examination on improving the assignation of these patients to prognostic groups and, consequently, making optimal therapeutic decisions. Methods: Predefined features were reviewed on histology slides from 39 GCT patients followed-up for a median of 48.28 months. We designed a uniform pathology protocol, focused on identifying potential new prognostic factors. Categorical and continuous variables were quantified using light microscopy and computer-aided morphometry and, due to the small sample size, their statistical correlation was analyzed by exact tests and Spearman’s rho, respectively. Significant (2-sided p-value <0.05, under sample size reserve) coefficient values were entered in hierarchical cluster analysis (HCA). Results: Favorable IGCCCG group, presence of seminoma, glandular tissue pattern, presence and histoarchitecture of lymphocytic infiltrate associated better survival rates and lower risk of progression. Invasion of the epididymis and spermatic cord, presence of teratoma, choriocarcinoma and yolk-sac elements, papillary pattern and cell pleomorphism predicted poorer outcomes. HCA yielded 2 significantly distinct patient groups in terms of overall survival (p=0.018) and time to progression (p=0.080), but not disease-free survival (p=0.614). Conclusions: Quantification of tumor subtypes and other histology features of GCTs (e.g. necrosis, tissue patterns, inflammation) is feasible and, if standardized, may prove useful in optimal selection of risk groups, when performed by an experienced pathologist.

SUBSPACES VERSUS SUBMANIFOLDS: A COMPARATIVE STUDY IN SMALL SAMPLE SIZE PROBLEM

2009 ◽  
Vol 23 (03) ◽  
pp. 463-490 ◽  
Author(s):  
HONG HUANG ◽  
JIANWEI LI ◽  
HAILIANG FENG
Keyword(s):  
Face Recognition ◽  
Comparative Study ◽  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Locally Linear Embedding ◽  
Small Sample Size Problem ◽  
Size Problem ◽  
The Face ◽  
Locally Linear

Automatic face recognition is a challenging problem in the biometrics area, where the dimension of the sample space is typically larger than the number of samples in the training set and consequently the so-called small sample size problem exists. Recently, neuroscientists emphasized the manifold ways of perception, and showed the face images may reside on a nonlinear submanifold hidden in the image space. Many manifold learning methods, such as Isometric feature mapping, Locally Linear Embedding, and Locally Linear Coordination are proposed. These methods achieved the submanifold by collectively analyzing the overlapped local neighborhoods and all claimed to be superior to such subspace methods as Eigenfaces and Fisherfaces in terms of classification accuracy. However, in literature, no systematic comparative study for face recognition is performed among them. In this paper, we carry out a comparative study in face recognition among them, and the study considers theoretical aspects as well as simulations performed using CMU PIE and FERET face databases.

Sign in / Sign up

Export Citation Format

Share Document