DMOS: a generic document recognition method, application to an automatic generator of musical scores, mathematical formulae and table structures recognition systems

2002 ◽  
Author(s):  
B. Couasnon
Keyword(s):  
Recognition Method ◽  
Document Recognition ◽  
Musical Scores ◽  
Recognition Systems

POSE-EXPRESSION NORMALIZATION FOR FACE RECOGNITION USING CONNECTED COMPONENTS ANALYSIS

2006 ◽  
Vol 20 (06) ◽  
pp. 869-881
Author(s):  
JAE-YOUNG CHOI ◽  
TAEG-KEUN WHANGBO ◽  
YOUNG-GYU YANG ◽  
MURLIKRISHNA VISWANATHAN ◽  
NAK-BIN KIM
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Connected Components ◽  
Recognition Method ◽  
Facial Region ◽  
Detection Model ◽  
Recognition Systems ◽  
Components Analysis ◽  
Empirical Tests ◽  
Connected Components Analysis

Accurate measurement of poses and expressions can increase the efficiency of recognition systems by avoiding the recognition of spurious faces. This paper presents a novel and robust pose-expression invariant face recognition method in order to improve the existing face recognition techniques. First, we apply the TSL color model for detecting facial region and estimate the vector X-Y-Z of face using connected components analysis. Second, the input face is mapped by a deformable 3D facial model. Third, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector and action unit of expression. Finally, the damaged regions which occur during the process of normalization are reconstructed using PCA. Several empirical tests are used to validate the application of face detection model and the method for estimating facial poses and expression. In addition, the tests suggest that recognition rate is greatly boosted through the normalization of the poses and expression.

scholarly journals An effective face recognition method using guided image filter and convolutional neural network

2021 ◽  
Vol 23 (3) ◽  
pp. 1699
Author(s):  
Yallamandaiah S. ◽  
Purnachand N.
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Recognition Method ◽  
Image Filter ◽  
Guided Image Filter ◽  
Face Region ◽  
The Face ◽  
Recognition Systems

<p>In the area of computer vision, face recognition is a challenging task because of the pose, facial expression, and illumination variations. The performance of face recognition systems reduces in an unconstrained environment. In this work, a new face recognition approach is proposed using a guided image filter, and a convolutional neural network (CNN). The guided image filter is a smoothing operator and performs well near the edges. Initially, the ViolaJones algorithm is used to detect the face region and then smoothened by a guided image filter. Later the proposed CNN is used to extract the features and recognize the faces. The experiments were performed on face databases like ORL, JAFFE, and YALE and attained a recognition rate of 98.33%, 99.53%, and 98.65% respectively. The experimental results show that the suggested face recognition method attains good results than some of the state-of-the-art techniques.</p>

scholarly journals RECOGNITION METHOD OF ELLIPTIC FORMS OBJECTS ON THE IMAGES

2019 ◽  
Vol 2019 (3) ◽  
pp. 4-8
Author(s):  
Евгений Леонов ◽  
Evgeny Leonov ◽  
Юрий Леонов ◽  
Yuriy Leonov ◽  
Андрей Аверченков ◽  
...  
Keyword(s):  
Mobile Devices ◽  
High Speed ◽  
Video Camera ◽  
Recognition System ◽  
Recognition Method ◽  
Computing Power ◽  
The Road ◽  
Road Signs ◽  
Recognition Systems ◽  
Intelligent Recognition

The article briefly describes the methodology and suggests the method for recognizing any elliptic forms objects on the images. This method is universal and can be applied in any intelligent recognition systems, for example, recognition system of the road signs from video camera images. The proposed method has proven itself in solving various practical problems, such as searching for signs in photographs, detecting circles on charts and diagrams, searching for the boundaries of ovals of faces, etc. The main advantage of the method is its extreme ease of implementation and high speed, which makes it possible to use not only on modern stationary computers, but also on mobile devices with low computing power.

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