Similarly high reliability between groups indicates FootSnap is appropriate for longitudinal follow-ups in diabetic feet, with potential for monitoring pathology.

No abstract

Micro-facial expressions are regarded as an important human behavioural event that can highlight emotional deception. Spotting these movements is difficult for humans and machines, however research into using computer vision to detect subtle facial expressions is growing in popularity. This paper proposes an individualised baseline micro-movement detection method using 3D Histogram of Oriented Gradients (3D HOG) temporal difference method. We define a face template consisting of 26 regions based on the Facial Action Coding System (FACS). We extract the temporal features of each region using 3D HOG. Then, we use Chi-square distance to find subtle facial motion in the local regions. Finally, an automatic peak detector is used to detect micro-movements above the newly proposed adaptive baseline threshold. The performance is validated on two FACS coded datasets: SAMM and CASME II. This objective method focuses on the movement of the 26 face regions. When comparing with the ground truth, the best result was an AUC of 0.7512 and 0.7261 on SAMM and CASME II, respectively. The results show that 3D HOG outperformed for micro-movement detection, compared to state-of-the-art feature representations: Local Binary Patterns in Three Orthogonal Planes and Histograms of Oriented Optical Flow.

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.INDEX TERMS Wrinkle detection, Hessian filter, line tracking, Bosphorus dataset, Jaccard similarity index.

No abstract