Abstract-This paper concerns the automatic recognition of human facial expressions using a fast 3D sensor, such as the Kinect. Facial expressions represent a rich source of information regarding emotion and interpersonal communication. The ability to recognize expressions automatically will have a large impact in many areas, particularly human-computer interaction. This paper describes 2 frameworks for recognizing 6 basic expressions using 3-dimensional data sequences that are captured in real time.Results are presented that demonstrate accuracy levels for the different techniques, and for different methods of preprocessing, registration and classification. We also describe the potential to use such a system for treatment of children with autism spectrum disorders (ASD).

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Small pulmonary nodules are radiologic findings that represent an important challenge in diagnosis systems. While these nodules are the major indicator for lung cancer and metastasis, their properties like size and location play an important role in classifying the benign one from the malignant. Estimating the growth rate of the nodule size states the degree of malignancy. This paper presents a computer-aided diagnosis (CAD) system to detect small-size pulmonary nodules from the chest computed tomography (CT) images through two dimensional (2-D) and three-dimensional (3-D) methods. Also, a computed volumetric growth is a promising way to distinguish malignant from nonmalignant pulmonary nodules. It was applied to lung nodules (2 to 7 mm in diameter) and achieved sensitivity 94.6% with an average; it is expected to aid radiologists in the detection of small nodules on thin-section multi-detector row CT images.