Speech quality and recognition rate improvement in car noise environments

2001 ◽  
Vol 37 (12) ◽  
pp. 800 ◽  
Author(s):  
S. Jeong ◽  
M. Hahn
Keyword(s):  
Recognition Rate ◽  
Speech Quality ◽  
Rate Improvement

Facial Expression Recognition Using Directional Local Binary Pattern

2014 ◽  
Vol 701-702 ◽  
pp. 395-399
Author(s):  
Ying Tong ◽  
Kun Wang ◽  
Liang Bao Jiao
Keyword(s):  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Gaussian White Noise ◽  
Recognition Rate ◽  
Local Deformation ◽  
Expression Recognition ◽  
Directional Information ◽  
Rate Improvement ◽  
Lbp Descriptor

Local binary pattern (LBP) descriptor could not efficiently describe the gray change in different directions of facial expressions characteristic regions. For this, the directional local binary pattern (DLBP) is put forward to represent facial geometrical characteristic. DLBP encodes the directional information of the face’s facial textures in horizontal, vertical and diagonal three directions, which can effectively describe the characteristic of facial muscles, wrinkles and other local deformation. Experimental results on JAFFE databases demonstrate the algorithm’s effectiveness, where nearly 5 percent recognition rate improvement is obtained beyond traditional LBP. Additional experiments verify robustness and reliability of the proposed DLBP operator within Gaussian white noise and pepper salt noise.

A Novel HOG Descriptor with Spatial Multi-Scale Feature for FER

2014 ◽  
Vol 596 ◽  
pp. 322-327
Author(s):  
Ying Tong ◽  
Liang Bao Jiao ◽  
Xue Hong Cao
Keyword(s):  
Processing Time ◽  
Recognition Rate ◽  
Spatial Arrangement ◽  
Scale Model ◽  
Layer By Layer ◽  
Canny Operator ◽  
Edge Information ◽  
Scale Feature ◽  
Multi Scale ◽  
Rate Improvement

HOG Feature is an efficient edge information descriptor, but it ignores the spatial arrangement of local FER features. In this respect, this paper puts forward a spatial multi-scale model based on an improved HOG algorithm which uses canny operator instead of traditional gradient operator. After the image is divided into a series of sub-regions layer by layer, the histogram of orient gradients for each sub-region is calculated and connected in sequence to obtain the spatial multi-scale HOG feature of whole image. Compared with traditional HOG and the improved PHOG, the proposed SMS_HOG algorithm acquires 5% recognition rate improvement and 50% processing time reduction.

scholarly journals LCSSA optimization for vectorization recognition rate improvement

2021 ◽  
Vol 1827 (1) ◽  
pp. 012143
Author(s):  
Mengyao Chen ◽  
Yunda Chai ◽  
Jiandong shang
Keyword(s):  
Recognition Rate ◽  
Rate Improvement

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

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