In this paper, we present a face recognition system using skin segmentation as feature reduction. Human Skin detection reduces the face region research in the image. In addition, color skin is a robust information face to rotation; scale and illumination variation. Skin regions are extracted using a set of bounding rules based on the skin color distribution obtained from a training set. The proposed RGB-YCbCr skin color model for skin region segmentation was evaluated on a set of three databases. The set was choice in a controlled and uncontrolled environment. Two classifiers are studied to build our face recognition system; the K nearest neighbor applied for each channel of the segmented image, whereas the second used the Principal component analysis PCA as reduction method followed by knn classifier. Best recognition rate were obtained for the first classifier.

Face recognition system using skin detection in RGB and YCbCr color space

This paper presents a vision based pose estimation system using knowledge based approach for human-robot symbiosis. The system is based on visual information of the face by connected component anal...

https://journals.sagepub.com/doi/pdf/10.5772/5663

On Pose Estimation for Human-Robot Symbiosis

This paper presents an automatic 3D head pose initialization scheme for a real-time face tracker with application to human-robot interaction. It has two main contributions. First, we propose an automatic 3D head pose and person specific face shape estimation, based on a 3D deformable model. The proposed approach serves to initialize our real-time 3D face tracker. What makes this contribution very attractive is that the initialization step can cope with faces under arbitrary pose, so it is not limited only to near-frontal views. Second, the previous framework is used to develop an application in which the orientation of an AIBO's camera can be controlled through the imitation of user's head pose. In our scenario, this application is used to build panoramic images from overlapping snapshots. Experiments on real videos confirm the robustness and usefulness of the proposed methods.

/pdf/single-snapshot-based-3d-head-pose-initialization-for-26fzkq6n3o.pdf

Single snapshot-based 3D head pose initialization for tracking in a HRI scenario

Hand Gesture (HG) recognition has been an interesting and challenging scientific problem in computer vision and machine learning. However, achieving high performance and high accuracy is a challenging task in gesture recognition. The intensive computation, the complex backgrounds, conditions of lights and positions are the most remarkable challenges regarding performance and accuracy. Those two aspects are taken into consideration in this paper. It proposes a convolutional neural network (CNN) model that can recognize three static HGs using a simpler architecture and fewer weights than previously proposed networks. The proposed CNN is completely implemented and several optimizations are performed to reduce runtime for frames with 28x28 pixels. The total runtime is 42 ms. Moreover, one of the layers is implemented on hardware(HW) level on ARM-FPGA SoC and it is faster than the software(SW) by 39.8x speedup. The total run time is 23.75 ms in C++/ VHDL. A comparison of the proposed model is made between our accelerated implementation and Caffe, which achieves a speedup of 1.17. The proposed model has an accuracy of 94%.

Hardware/Software-Codesign for Hand Gestures Recognition using a Convolutional Neural Network

Sociorobots represent a class of ‘conversational artificial beings’ which can be used for social caring of people needing it, for entertainment, and in many cases for research purposes. The aim of this chapter is to deal with a number of conceptual and practical issues of sociorobot design complementary to those discussed in other chapters of the book. Specifically, the chapter discusses the general sociorobot design, embodiment, and morphology aspects, investigates more closely the human-oriented robot perception (person tracking, face and gesture recognition, speech recognition), presents the concept of ‘design pattern’ as applied to sociorobots, and provides a brief account of sociorobot ethical issues, viz., attachment, deception, awareness, robot authority, user autonomy, user privacy, justice, and robot mediated human-human interaction.

Complementary Sociorobot Issues

This chapter presents a vision-based face and gesture recognition system for human-robot interaction. By using subspace method, face and predefined hand poses are classified from the three largest skin-like regions that are segmented using YIQ color representation system. In the subspace method we consider separate eigenspaces for each class or pose. Face is recognized using pose specific subspace method and gesture is recognized using the rulebased approach whenever the combinations of three skin-like regions at a particular image frame satisfy a predefined condition. These gesture commands are sent to robot through TCP/IP wireless network for human-robot interaction. The effectiveness of this method has been demonstrated by interacting with an entertainment robot named AIBO and a humanoid robot Robovie. Human-robot symbiotic systems have been studied extensively in recent years, considering that robots will play an important role in the future welfare society [Ueno, 2001]. The use of intelligent robots encourages the view of the machine as a partner in communication rather than as a tool. In the near future, robots will interact closely with a group of humans in their everyday environment in the field of entertainment, recreation, health-care, nursing, etc. In human-human interaction, multiple communication modals such as speech, gestures and body movements are frequently used. The standard input methods, such as text input via the keyboard and pointer/location information from a mouse, do not provide a natural, intuitive interaction between humans and robots. Therefore, it is essential to create models for natural and intuitive communication between humans and robots. Furthermore, for intuitive gesture-based interaction between human and robot, the robot should understand the meaning of gesture with respect to society and culture. The ability to understand hand gestures will improve the naturalness and efficiency of human interaction with robot, and allow the user to communicate in complex tasks without using tedious sets of detailed instructions. This interactive system uses robot eye’s cameras or CCD cameras to identify humans and recognize their gestures based on face and hand poses. Vision-based face recognition systems have three major components: image processing or extracting important clues (face pose and position), tracking the facial features (related position or motion of face and hand poses), and face recognition. Vision-based face recognition system varies along a number of

/pdf/face-and-gesture-recognition-for-human-robot-interaction-3zfawvwlfb.pdf

Haruki Ueno

Papers

Face and Gesture Recognition for Human-Robot Interaction