Temporal face feature progression with cycle GAN

The aging process creates significant changes in the appearances of people’s faces. When compared to other causes of variation in face imaging, aging-related variation has specific distinct properties. Facial Aging variations, for example, is unique for each person; it occurs gradually and is significantly influenced by other characteristics including health, gender, and life-style. As a result, the proposed effort will use Generative Adversarial Networks to address these critical concerns (GANs). Generative Adversarial Networks (GAN’s) is made up of a generator and a discriminator network. The generator model generates images that a discriminator model analyses to determine if they are real or fake. This paper provides a Temporal Face Feature Progressive framework with Cycle GAN, which maintains the initial appearance and identity in the elderly aspect of their facial structure. To address aging concerns, our goal is to transform an initial age category image into a targeted age with age progression. We show that our temporal face features progressive cycle GAN learns and transfers facial traits from the source group to the targeted group by training various images. The IMDB-WIKI Face dataset has been used to obtain the results for the same.

Racial Identity-Aware Facial Expression Recognition Using Deep Convolutional Neural Networks

Multi-culture facial expression recognition remains challenging due to cross cultural variations in facial expressions representation, caused by facial structure variations and culture specific facial characteristics. In this research, a joint deep learning approach called racial identity aware deep convolution neural network is developed to recognize the multicultural facial expressions. In the proposed model, a pre-trained racial identity network learns the racial features. Then, the racial identity aware network and racial identity network jointly learn the racial identity aware facial expressions. By enforcing the marginal independence of facial expression and racial identity, the proposed joint learning approach is expected to be purer for the expression and be robust to facial structure and culture specific facial characteristics variations. For the reliability of the proposed joint learning technique, extensive experiments were performed with racial identity features and without racial identity features. Moreover, culture wise facial expression recognition was performed to analyze the effect of inter-culture variations in facial expression representation. A large scale multi-culture dataset is developed by combining the four facial expression datasets including JAFFE, TFEID, CK+ and RaFD. It contains facial expression images of Japanese, Taiwanese, American, Caucasian and Moroccan cultures. We achieved 96% accuracy with racial identity features and 93% accuracy without racial identity features.

Face Swapping Consistency Transfer with Neural Identity Carrier

Deepfake aims to swap a face of an image with someone else’s likeness in a reasonable manner. Existing methods usually perform deepfake frame by frame, thus ignoring video consistency and producing incoherent results. To address such a problem, we propose a novel framework Neural Identity Carrier (NICe), which learns identity transformation from an arbitrary face-swapping proxy via a U-Net. By modeling the incoherence between frames as noise, NICe naturally suppresses its disturbance and preserves primary identity information. Concretely, NICe inputs the original frame and learns transformation supervised by swapped pseudo labels. As the temporal incoherence has an uncertain or stochastic pattern, NICe can filter out such outliers and well maintain the target content by uncertainty prediction. With the predicted temporally stable appearance, NICe enhances its details by constraining 3D geometry consistency, making NICe learn fine-grained facial structure across the poses. In this way, NICe guarantees the temporal stableness of deepfake approaches and predicts detailed results against over-smoothness. Extensive experiments on benchmarks demonstrate that NICe significantly improves the quality of existing deepfake methods on video-level. Besides, data generated by our methods can benefit video-level deepfake detection methods.

Structural Properties of Faces of the Cone of Copositive Matrices

In this paper, we study the properties of faces and exposed faces of the cone of copositive matrices (copositive cone), paying special attention to issues related to their geometric structure. Based on the concepts of zero and minimal zero vectors, we obtain several explicit representations of faces of the copositive cone and compare them. Given a face of the cone of copositive matrices, we describe the subspace generated by that face and the minimal exposed face containing it. Summarizing the results obtained in the paper, we systematically show what information can be extracted about the given copositive face in the case of incomplete data. Several examples for illustrating the main findings of the paper and also for justifying the usefulness of the developed approach to the study of the facial structure of the copositive cone are discussed.

FABRICATION OF OCULAR PROSTHESIS IN A SINGLE APPOINTMENT USING A CUSTOM PAINTED IRIS- A CASE REPORT.

Loss of any facial structure can have a deep social, physical and psychological impact on the patient. Rehabilitation of such patients with ocular prosthesis helps to overcome not only their psychological trauma but also social acceptance by restoring the lost facial structure and aesthetics of patient. The present article describes a method of fabricating ocular prosthesis in a single appointment by using conventional materials for acceptable t and optimum aesthetic treatment outcome

MEDIAN LINE IMMETRITY AT CENTRIC OCCLUSSION IN FKG UNMAS DENPASAR

Introduction: Facial appearances especially teeth and face are some of the most important things in this era. The symmetry of the dental median line with the facial median line needs to be considered to create a balanced, harmonious, and attractive facial structure, especially when smiling. The position of the maxillary median line to the facial median line is an important factor in orthodontic diagnosis. This research aims to obtain the symmetry of the dental median line with the facial median line during centric occlusion in dentistry students of the Mahasaraswati University of Denpasar. Materials and Methods: The type of this research is descriptive with a cross-sectional approach that involved 57 participants. Results and Discussions: The results of this research are the percentage of the students whose dental median line with a facial median line is symmetrical (≤ 1 mm) was 77% that obtained 44 participants, while the percentage of students who had a dental median line with a facial median line is not symmetrical (> 1 mm) were 23 % that obtained 13 participants with a shift of median line 2 mm in 7 participants, 3 mm in 5 participants, and 5 mm in 1 participant. Conclusion: Based on the results it can be concluded that the dentistry students of the Mahasaraswati University of Denpasar whose dental median line with their facial median line were symmetrical is more dominant.