scholarly journals A Face Image Database for Evaluating Out-of-Focus Blur

10.5772/17977 ◽  
2011 ◽  
Author(s):  
Qi Han ◽  
Qiong Li ◽  
Xiamu Niu
Keyword(s):  
Image Database ◽  
Face Image

scholarly journals CASIA-Face-Africa: A Large-scale African Face Image Database

2021 ◽  
pp. 1-1
Author(s):  
Jawad Muhammad ◽  
Yunlong Wang ◽  
Caiyong Wanga ◽  
Kunbo Zhang ◽  
Zhenan Sun
Keyword(s):  
Large Scale ◽  
Image Database ◽  
Face Image

Study of Face Feature Extraction and Recognition Techniques Based on Wavelet Analysis

2014 ◽  
Vol 1030-1032 ◽  
pp. 1810-1813
Author(s):  
Xin Wang ◽  
He Pan
Keyword(s):  
Face Recognition ◽  
Wavelet Decomposition ◽  
Feature Vector ◽  
Recognition Performance ◽  
Image Database ◽  
Face Image ◽  
Support Vector ◽  
Svm Model ◽  
Feature Based ◽  
Vector Dimension

Face recognition is a research hotspot of pattern recognition and artificial intelligence. This paper presents a method of extract face feature based on Wavelet. First, reduce vector dimension by wavelet decomposition of the image, second, train the multi class support vector machine (SVM) model by face feature vector extracted and make face recognition finally. The experiments on ORL face image database of the algorithm shows the superiority of the proposed algorithm in terms of recognition performance.

scholarly journals Feature Guided CNN for Baby’s Facial Expression Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Qing Lin ◽  
Ruili He ◽  
Peihe Jiang
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Image Database ◽  
Face Image ◽  
Expression Recognition ◽  
Human Beings ◽  
Face Datasets

State-of-the-art facial expression methods outperform human beings, especially, thanks to the success of convolutional neural networks (CNNs). However, most of the existing works focus mainly on analyzing an adult’s face and ignore the important problems: how can we recognize facial expression from a baby’s face image and how difficult is it? In this paper, we first introduce a new face image database, named BabyExp, which contains 12,000 images from babies younger than two years old, and each image is with one of three facial expressions (i.e., happy, sad, and normal). To the best of our knowledge, the proposed dataset is the first baby face dataset for analyzing a baby’s face image, which is complementary to the existing adult face datasets and can shed some light on exploring baby face analysis. We also propose a feature guided CNN method with a new loss function, called distance loss, to optimize interclass distance. In order to facilitate further research, we provide the benchmark of expression recognition on the BabyExp dataset. Experimental results show that the proposed network achieves the recognition accuracy of 87.90% on BabyExp.

Multi-Level Search Space Reduction Framework for Face Image Database

2015 ◽  
Vol 11 (1) ◽  
pp. 12-29 ◽  
Author(s):  
C. Sweetlin Hemalatha ◽  
V. Vaidehi ◽  
K. Nithya ◽  
A. Annis Fathima ◽  
M. Visalakshi ◽  
...  
Keyword(s):  
Face Recognition ◽  
Large Scale ◽  
Search Time ◽  
Image Database ◽  
Search Space ◽  
Face Image ◽  
Tree Model ◽  
Space Reduction ◽  
Search Space Reduction ◽  
Multi Level

In face recognition, searching and retrieval of relevant images from a large database form a major task. Recognition time is greatly related to the dimensionality of the original data and the number of training samples. This demands the selection of discriminant features that produce similar results as the entire set and a reduced search space. To address this issue, a Multi-Level Search Space Reduction framework for large scale face image database is proposed. The proposed approach identifies discriminating features and groups face images sharing similar properties using feature-weighted Fuzzy C-Means approach. A hierarchical tree model is then constructed inside every cluster based on the discriminating features which enables a branch based selection, thereby reducing the search space. The proposed framework is tested on three benchmark and two self-created databases. The experimental results show that the proposed method achieved an average accuracy of 93% and an average search time reduction of 66% compared to existing approaches for search space reduction of face recognition.

scholarly journals Automatic Adjustment of Facial Orientation in 3D Face Image Database

2003 ◽  
Vol 8 (1) ◽  
pp. 41-47 ◽  
Author(s):  
Mineo Yoshino ◽  
Kazuhiko Imaizumi ◽  
Toyohisa Tanijiri ◽  
John G. Clement
Keyword(s):  
Image Database ◽  
Face Image ◽  
3D Face ◽  
Automatic Adjustment ◽  
Facial Orientation

The Image Database Management System of Teaching File Using Personal Computer

1995 ◽  
Vol 32 (4) ◽  
pp. 677
Author(s):  
M J Shin ◽  
G W Kim ◽  
T J Chun ◽  
W H Ahn ◽  
S K Balk ◽  
...  
Keyword(s):  
Personal Computer ◽  
Management System ◽  
Database Management ◽  
Image Database ◽  
Database Management System ◽  
Teaching File
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