Wrinkle detection using hessian line tracking

Wrinkles play an important role in 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 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 identified problem. The proposed 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 intra- and inter-reliability were measured, with an accuracy of 94% or above. Experimental results show that the proposed method is capable of tracking hidden pixels; thus it increases connectivity of detection in between wrinkles, allowing some fine wrinkles to be detected. In comparison to the stateof- 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 work demonstrates that HLT is a remarkably strong detector of forehead wrinkles in 2D images.