Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks

This paper addresses the problem of continuous gesture recognition from sequences of depth maps using convolutional neutral networks (ConvNets). The proposed method first segments individual gestures from a depth sequence based on quantity of movement (QOM). For each segmented gesture, an Improved Depth Motion Map (IDMM), which converts the depth sequence into one image, is constructed and fed to a ConvNet for recognition. The IDMM effectively encodes both spatial and temporal information and allows the fine-tuning with existing ConvNet models for classification without introducing millions of parameters to learn. The proposed method is evaluated on the Large-scale Continuous Gesture Recognition of the ChaLearn Looking at People (LAP) challenge 2016. It achieved the performance of 0.2655 (Mean Jaccard Index) and ranked $3^{rd}$ place in this challenge

Performance Improvement of Data Fusion Based Real-Time Hand Gesture Recognition by Using 3-D Convolution Neural Networks With Kinect V2

Hand gesture recognition is one of the most active areas of research in computer vision. It provides an easy way to interact with a machine without using any extra devices. Hand gestures are natural and intuitive communication way for the human being to interact with his environment. In this paper, we propose Data Fusion Based Real-Time Hand Gesture Recognition using 3-D Convolutional Neural Networks and Kinect V2. To achieve the accurate segmentation and tracking with Kinect V2. Convolution neural network to improve the validity and robustness of the system. Based on the experimental results, the proposed model is accurate, robust and performance with very low processor utilization. The performance of our proposed system in real life application, which is controlling various devices using Kinect V2. Keywords: Hand gesture recognition, Kinect V2, data fusion, Convolutional Neural Networks DOI: 10.7176/IKM/9-1-02

DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling

Face modeling has been paid much attention in the field of visual computing. There exist many scenarios, including cartoon characters, avatars for social media, 3D face caricatures as well as face-related art and design, where low-cost interactive face modeling is a popular approach especially among amateur users. In this paper, we propose a deep learning based sketching system for 3D face and caricature modeling. This system has a labor-efficient sketching interface, that allows the user to draw freehand imprecise yet expressive 2D lines representing the contours of facial features. A novel CNN based deep regression network is designed for inferring 3D face models from 2D sketches. Our network fuses both CNN and shape based features of the input sketch, and has two independent branches of fully connected layers generating independent subsets of coefficients for a bilinear face representation. Our system also supports gesture based interactions for users to further manipulate initial face models. Both user studies and numerical results indicate that our sketching system can help users create face models quickly and effectively. A significantly expanded face database with diverse identities, expressions and levels of exaggeration is constructed to promote further research and evaluation of face modeling techniques.Comment: 12 pages, 16 figures, to appear in SIGGRAPH 201