The relationship between face and disease has been discussed from thousands years ago, which leads to the occurrence of facial diagnosis. The objective here is to explore the possibility of identifying diseases from uncontrolled 2D face images by deep learning techniques. In this paper, we propose using deep transfer learning from face recognition to perform the computer-aided facial diagnosis on various diseases. In the experiments, we perform the computer-aided facial diagnosis on single (beta-thalassemia) and multiple diseases (beta-thalassemia, hyperthyroidism, Down syndrome, and leprosy) with a relatively small dataset. The overall top-1 accuracy by deep transfer learning from face recognition can reach over 90% which outperforms the performance of both traditional machine learning methods and clinicians in the experiments. In practical, collecting disease-specific face images is complex, expensive and time consuming, and imposes ethical limitations due to personal data treatment. Therefore, the datasets of facial diagnosis related researches are private and generally small comparing with the ones of other machine learning application areas. The success of deep transfer learning applications in the facial diagnosis with a small dataset could provide a low-cost and noninvasive way for disease screening and detection.

Kinect is a device introduced in November 2010 as an accessory of Xbox 360. The acquired data has different and complementary natures, combining geometry with visual attributes. For this reason, Kinect is a flexible tool that can be used in applications from several areas such as: Computer Graphics, Image Processing, Computer Vision and Human-Machine Interaction. In this way, the Kinect is a widely used device in industry (games, robotics, theater performers, natural interfaces, etc.) and in research. We will initially present some concepts about the device: the architecture and the sensor. We then will discuss about the data acquisition process: capturing, representation and filtering. Capturing process consists of obtaining a colored image (RGB) and performing a depth measurement (D), with structured light technique. This data is represented by a structure called RGBD Image. We will also talk about the main tools available for developing applications on various platforms. Furthermore, we will discuss some recent projects based on RGBD Images. In particular, those related to Object Recognition, 3D Reconstruction, Augmented Reality, Image Processing, Robotic, and Interaction. In this survey, we will show some research developed by the academic community and some projects developed for the industry. We intend to show the basic principles to begin developing applications using Kinect, and present some projects developed at the VISGRAF Lab. And finally, we intend to discuss the new possibilities, challenges and trends raised by Kinect.

Various modern security systems follow a tendency to simplify the usage of the existing biometric recognition solutions and embed them into ubiquitous portable devices. In this work, we continue the investigation and development of our method for securing identification documents. The original facial biometric template, which is extracted from the trusted frontal face image, is stored on the identification document in a secured personalized machine-readable code. Such document is protected from face photo manipulation and may be validated with an offline mobile application. We apply automatic methods of compressing the developed face descriptors to make the biometric validation system more suitable for mobile applications. As an additional contribution, we introduce several print-capture datasets that may be used for training and evaluating similar systems for mobile identification and travel documents validation.

Geometric Modeling is a widely studied area in computer graphics and methods for constructing 3D models with intuitive interfaces are a topic that has been attracting the interest of many researches. In contrast to the complicated interfaces of modeling softwares, created using the WIMP paradigm (Window, Icon, Menu, Pointer), several studies have shown applications with interfaces based on gestures, which are simpler and more natural. In this scenario, the area of Sketch-Based Interfaces and Modeling (SBIM) emerged. Sketch is a very efficient tool to convey the essence of an object with few strokes, because humans have the ability of inferring 3D models from 2D drawings. However, associating a sketch to a 3D model is not a computationally trivial task. In this paper we present a conceptualization of the area, highlighting opportunities, challenges and trends in SBIM.

Classical music appreciation is non trivial. Visual representation can aid music teaching and learning processes. In this sense, we propose a set of visual representations based on musical notes features, such as: note type, octave, velocity and timbre. In our system, the visual elements appear along with their corresponding musical elements, in order to improve the perception of musical structures. The visual representations we use to enhance the comprehension of a composition could be extended to performing arts scenarios. It could be adopted as motion graphics during live musical performances. We have developed several videos to illustrate our method. We have also developed an ear training quiz and a research questionnaire. This material is available at http://www.impa.br/~vitorgr/VLMU/home.html .