Automatic landmark annotation and dense correspondence registration for 3D human facial images

Dense surface registration of three-dimensional (3D) human facial images holds great potential for studies of human trait diversity, disease genetics, and forensics. Non-rigid registration is particularly useful for establishing dense anatomical correspondences between faces. Here we describe a novel non-rigid registration method for fully automatic 3D facial image mapping. This method comprises two steps: first, seventeen facial landmarks are automatically annotated, mainly via PCA-based feature recognition following 3D-to-2D data transformation. Second, an efficient thin-plate spline (TPS) protocol is used to establish the dense anatomical correspondence between facial images, under the guidance of the predefined landmarks. We demonstrate that this method is robust and highly accurate, even for different ethnicities. The average face is calculated for individuals of Han Chinese and Uyghur origins. While fully automatic and computationally efficient, this method enables high-throughput analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl

Own-race and other-race face recognition problems without visual expertise problems in dyslexic readers

Post-print (lokagerð höfundar)Both intact and deficient neural processing of faces has been found in dyslexic readers. Similarly, behavioral studies have shown both normal and abnormal face processing in developmental dyslexia. We tested whether dyslexic adults are impaired in tests of own-race and other-race face recognition. As both face and word recognition rely considerably on visual expertise, we wished to investigate whether face recognition problems of dyslexic readers might stem from difficulties with experience-driven expert visual processing. We utilized the finding that people tend to be worse at discriminating other-race faces compared to own-race faces, the so-called other-race effect, thought to reflect greater experience with own-race faces. If visual expertise is compromised in dyslexic readers, so that their visual system is not effectively shaped by experience, then they might show a diminished other-race effect. Matched dyslexic and typical readers completed two tests of own- and other-race face recognition. The results show that dyslexic readers have problems with recognizing faces, and these difficulties are not fully accounted for by general problems with attention or memory. However, recognition is compromised for both own- and other-race faces, and the strength of the other-race effect does not differ between dyslexic and typical readers. There was individual variability in both groups, and an exploratory analysis revealed that while dyslexic readers with no university education showed deficits in face recognition, the dyslexic participants with higher education did not. We conclude that dyslexic readers as a group have face recognition problems. These are potentially modulated by educational level but compromised visual expertise cannot demonstrably account for the face recognition problems associated with dyslexia. We discuss the implications of these findings for theoretical accounts of dyslexia and for theories of word and face recognition.This work was supported by The Icelandic Research Fund (Grant No. 174013-051) and the University of Iceland Research Fund.Accepted peer-reviewed manuscrip

3D Shape Descriptor-Based Facial Landmark Detection: A Machine Learning Approach

Facial landmark detection on 3D human faces has had numerous applications in the literature such as establishing point-to-point correspondence between 3D face models which is itself a key step for a wide range of applications like 3D face detection and authentication, matching, reconstruction, and retrieval, to name a few. Two groups of approaches, namely knowledge-driven and data-driven approaches, have been employed for facial landmarking in the literature. Knowledge-driven techniques are the traditional approaches that have been widely used to locate landmarks on human faces. In these approaches, a user with sucient knowledge and experience usually denes features to be extracted as the landmarks. Data-driven techniques, on the other hand, take advantage of machine learning algorithms to detect prominent features on 3D face models. Besides the key advantages, each category of these techniques has limitations that prevent it from generating the most reliable results. In this work we propose to combine the strengths of the two approaches to detect facial landmarks in a more ecient and precise way. The suggested approach consists of two phases. First, some salient features of the faces are extracted using expert systems. Afterwards, these points are used as the initial control points in the well-known Thin Plate Spline (TPS) technique to deform the input face towards a reference face model. Second, by exploring and utilizing multiple machine learning algorithms another group of landmarks are extracted. The data-driven landmark detection step is performed in a supervised manner providing an information-rich set of training data in which a set of local descriptors are computed and used to train the algorithm. We then, use the detected landmarks for establishing point-to-point correspondence between the 3D human faces mainly using an improved version of Iterative Closest Point (ICP) algorithms. Furthermore, we propose to use the detected landmarks for 3D face matching applications

Three Dimensional (3D) Forensic Facial Reconstruction in an Egyptian Population using Computed Tomography Scanned Skulls and Average Facial Templates: A Study Examining Subjective and Objective Assessment Methods of 3D Forensic Facial Reconstructions

PhDForensic facial reconstruction can assist identification by reconstructing a face of the unknown person with the aim of its recognition by his/her family or friends. In the facial reconstruction approach adopted in this study, a 3D average face template was digitally warped onto a 3D scanned skull image. This study was carried out entirely on an Egyptian population, and was the first of its kind. Aims: This study aimed to demonstrate that 3D facial reconstructions using the novel methodology described could show significant resemblance to the faces corresponding to the persons in question when they were alive. Moreover, using techniques previously validated for facial reconstruction, the aim was to compare them to the method developed, and to assess approaches used to determine the accuracy of 3D facial reconstructions. Methods: Initially, a pilot study was conducted using a database of laser scanned skulls and faces. The faces were reconstructed using an average facial template generated by merging a number of faces of similar population, sex, and age. The applicability, as well as the main components of the facial reconstruction method, the single and average facial templates, and the facial soft tissue thickness measurements, were investigated. Furthermore, in the main study, the faces of computed tomography (CT) scanned heads of an Egyptian population were reconstructed using average facial templates. The accuracy of the reconstructed faces was assessed subjectively by face pool, and face resemblance tests, and objectively by measuring the surface distances between the real and reconstructed faces. In addition, a number of novel subjective and objective assessment methods were developed. These included assessment of individual facial regions using subjective resemblance scores, and objective surface distance comparisons. A new objective method, craniofacial anthropometry, was developed by taking and comparing direct measurements from the skull, and comparing the measurements from the real and reconstructed faces. The studied cases were ranked according to all subjective, and objective, tests, and statistically correlated. Results and Conclusions: The average facial templates showed a higher identification rate than the single face templates. The approach of facial reconstruction used in this thesis showed a comparable accuracy to many other facial reconstruction methods, yet was superior in terms of its applicability, transferability, and ease of use. In the face pool tests, the younger assessors were able to correctly identify the reconstructed faces better than older assessors. Furthermore, the identification rate by the forensic anthropology experts was higher than the non-experts. The former group showed the highest agreement between the observers in giving the resemblance scores. Although there was a significant rank correlation between the subjective and objective assessment tests, the subjective tests are influenced by the assessors’ subjective characteristics (e.g., age, professional experience), thus making objective assessment more reliable. However, in situations where subjective tests are used, it is better to use the face resemblance tests and consult forensic anthropologists. Also, Craniofacial Anthropometry, particularly the craniofacial angles, can successfully indicate the accuracy of the facial reconstructions. Importantly, this study shows that certain facial regions, particularly the cheek and the jaw, are more reliable than other areas in the subjective and objective assessment of the facial reconstructionEgyptian Ministry of Higher Education and the Egyptian Cultural Affairs and the Mission Sector, as well as the Egyptian cultural Counsellor and the staff of the Egyptian Cultural Centre and Educational Bureau in London, UK