Hand detection and segmentation using multimodal information from Kinect

Pham, Van-Tien; Le, Thi‐Lan; Tran, Thanh-Hai; Nguyễn, Thanh Phương

doi:10.1109/mapr49794.2020.9237785

Cited by 4 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section presents a brief about the contribution of existing literature towards hand geometry identification and classification emphasizing on segmentation process. The work carried out by Yang et al [14], Pham et al [15], and Van et al [16] have recently presented a simplified form of implementation towards segmentation considering palm image. The idea is meant for performing identification of user hand for authentication purposes.…”

Section: Related Workmentioning

confidence: 99%

Novel Optimization of Identified Palm Geometry Using Image Segmentation

Mahalakshmi¹,

Sheela²

2022

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Segmentation is one of the essential steps towards the identification of any object in the domain of image processing. In the area of hand-based biometric which is mainly deployed for a user authentication system, segmentation plays a critical role. A review of existing studies shows that there is a very less amount potential contribution in this regard. Therefore, this manuscript presents a novel optimization scheme towards palm geometry recognition system where segmentation process is the prime highlights for classification of hand and background considering a case study of finger recognition. Further, the proposed scheme uses masking operation where the Region-of-Interest section of hand is subjected to segmentation. Further proposed system uses machine learning approach (convolution neural network and Siamese Neural Network) to further assist in optimizing the segmentation performance. The experimental outcome of the study shows proposed system offers better accuracy compared to the existing system.

show abstract

Section: Related Workmentioning

confidence: 99%