Name
Abstract | ||
|---|---|---|
Wrinkles play an important role in the face-based analysis. They have been widely used in applications, such as facial retouching, facial expression recognition, and face age estimation. Although a few techniques for a wrinkle analysis have been explored in the literature, poor detection limits the accuracy and reliability of wrinkle segmentation. Therefore, an automated wrinkle detection method is crucial to maintain consistency and reduce human error. In this paper, we propose Hessian line tracking (HLT) to overcome the detection problem. HLT is composed of Hessian seeding and directional line tracking. It is an extension of a Hessian filter; however, it significantly increases the accuracy of wrinkle localization when compared with existing methods. In the experimental phase, three coders were instructed to annotate wrinkles manually. To assess the manual annotation, both intrareliability and interreliability were measured, with an accuracy of 94% or above. The experimental results show that the proposed method is capable of tracking hidden pixels; thus, it increases connectivity of detection between wrinkles, allowing some fine wrinkles to be detected. In comparison to the state-of-the-art methods such as the Cula Method, Frangi Filter, and Hybrid Hessian Filter, the proposed HLT yields better results, with an accuracy of 84%. This paper demonstrates that the HLT is a remarkably strong detector of forehead wrinkles in 2-D images. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ACCESS.2015.2455871 | Access, IEEE |
Keywords | Field | DocType |
Bosphorus dataset,Hessian filter,Jaccard similarity index,Wrinkle detection,line tracking | Facial recognition system,Computer vision,Wrinkle,Facial expression recognition,Computer science,Segmentation,Manual annotation,Hessian matrix,Artificial intelligence,Pixel,Detector | Journal |
Volume | ISSN | Citations |
3 | 2169-3536 | 5 |
PageRank | References | Authors |
0.46 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Choon-Ching Ng | 1 | 39 | 6.64 |
| Moi Hoon Yap | 2 | 190 | 27.82 |
| Nicholas Costen | 3 | 228 | 28.42 |
| Baihua Li | 4 | 176 | 21.71 |