Orthogonal least square and optimized support vector machine with polynomial kernel for classifying asphyxiated infant cry

Sahak, Rohilah; Mansor, W.; Lee, Y. K.; Zabidi, Azlee; Yassin, Ahmad Ihsan Mohd

doi:10.1109/icsipa.2011.6144159

Cited by 4 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to previous research, employment and performance of the selection methods of PCA varies with the respective research area. In cases like classification of infant cry (Sahak et al 2011), the eigenvalue-one-criterion excelled than the other two methods, however in other cases such as the texture of SAR image (Chamundeeswari & Singh 2009), the SCREE test was selected as the best method.…”

Section: Theoretical Background Principal Component Analysis (Pca)mentioning

confidence: 99%

Kinect-Based Human Gait Recognition Using Locally Linear Embedded and Support Vector Machine

Tahir¹,

Sahak²,

Yassin³

et al. 2018

JKUKM

View full text Add to dashboard Cite

Recognition of human gait could be performed effectively provided that significant gait features are well extracted along with effective recognition process. Thus, the gait features should be selected or optimized appropriately for optimal accuracy during recognition. Therefore, in this research, optimization of gait features for both oblique and frontal view are evaluated for recognition purpose using Locally Linear Embedded (LLE) along with multi-class Support Vector Machine (SVM). Firstly, dynamic gait features for one gait cycle are extracted from each subject's walking gait that is acquired using Kinect sensor. Next, the extracted gait features were then optimized using LLE known as DG-LLE and further classified by multi-class SVM with Error Correcting Output Code (ECOC) algorithm. Further, to validate the effectiveness of LLE as optimization technique, the proposed method is then compared with another two gait features namely the original gait features known as DG and optimization using Principal Component Analysis labeled as DG-PCA. Results showed that the optimization based on DG-LLE outperformed the other two methods namely DG and DG-PCA for both oblique and frontal views. In addition, DG-LLE method contributed as the highest recognition rate for both frontal and oblique views. Results also confirmed that the accuracy rate for frontal view is higher specifically 98.33% as compared to oblique view with 94.67%.

show abstract

Section: Theoretical Background Principal Component Analysis (Pca)mentioning

confidence: 99%

Kinect-Based Human Gait Recognition Using Locally Linear Embedded and Support Vector Machine

Tahir¹,

Sahak²,

Yassin³

et al. 2018

JKUKM

View full text Add to dashboard Cite

show abstract

Current Methods in Gait Analysis and Recognition: A Review on Model –Based Approach

Sahak

Tahir

Yassin

et al. 2018

2018 8th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

View full text Add to dashboard Cite

Human gait recognition based on frontal view using kinect features and orthogonal least square selection

Sahak

Tahir

Yassin

et al. 2017

2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

View full text Add to dashboard Cite

Orthogonal least square and optimized support vector machine with polynomial kernel for classifying asphyxiated infant cry

Cited by 4 publications

References 8 publications

Kinect-Based Human Gait Recognition Using Locally Linear Embedded and Support Vector Machine

Kinect-Based Human Gait Recognition Using Locally Linear Embedded and Support Vector Machine

Current Methods in Gait Analysis and Recognition: A Review on Model –Based Approach

Human gait recognition based on frontal view using kinect features and orthogonal least square selection

Contact Info

Product

Resources

About