Multiple-view face hallucination by a novel regression analysis in tensor space

Author

Sanguansat, Parinya

Source

The International Arab Journal of Information Technology

Issue

Vol. 13, Issue 6 (31 Dec. 2016)8 p.

Publisher

Zarqa University

Publication Date

2016-12-31

Country of Publication

Jordan

No. of Pages

8

Main Subjects

Information Technology and Computer Science

Topics

Abstract EN

In this paper, the n^^el multiple-view face hallucination method was proposed.

This method is reconstructed the high-resolution face images in various poses (normal, up, down, left, and right) from a single low-resolution face image within these poses.

There are two steps in our proposed method.

In the first step, a high-resolution face image in the same view of the observation is reconstructed by the position-patch face hallucination framework with the improved Locally Linear Embedding (LLE), which the number of neighbours is adaptive.

In the second step, the reconstructed image is used to generate the high-resolution of the other views by the novel tensor regression technique.

The experimental results on the well-known dataset show that the proposed method can achieve the better quality image than the baseline methods

American Psychological Association (APA)

Sanguansat, Parinya. 2016. Multiple-view face hallucination by a novel regression analysis in tensor space. The International Arab Journal of Information Technology،Vol. 13, no. 6.
https://search.emarefa.net/detail/BIM-654845

Modern Language Association (MLA)

Sanguansat, Parinya. Multiple-view face hallucination by a novel regression analysis in tensor space. The International Arab Journal of Information Technology Vol. 13, no. 6 (Dec. 2016).
https://search.emarefa.net/detail/BIM-654845

American Medical Association (AMA)

Sanguansat, Parinya. Multiple-view face hallucination by a novel regression analysis in tensor space. The International Arab Journal of Information Technology. 2016. Vol. 13, no. 6.
https://search.emarefa.net/detail/BIM-654845

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-654845