Hand gesture recognition and tracking based on distributed locally linear embedding

Ge, Shuzhi Sam; Yang, Yuxiang; Lee, T. H.

doi:10.1016/j.imavis.2008.03.004

Cited by 79 publications

(22 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A methodology called neighborhood linear embedding (NLE) [10] has been developed to discover the intrinsic property of the input data which is an adaptive scheme without the trial and error process in LLE. We modify the LLE algorithm and propose a new DLLE to discover the inherent properties of the input data [13].…”

Section: Nonlinear Dimension Reduction (Ndr) Methods For Person Depenmentioning

confidence: 99%

Facial expression recognition and tracking for intelligent human-robot interaction

Yang

Lee

et al. 2008

Intel Serv Robotics

Self Cite

View full text Add to dashboard Cite

For effective interaction between humans and socially adept, intelligent service robots, a key capability required by this class of sociable robots is the successful interpretation of visual data. In addition to crucial techniques like human face detection and recognition, an important next step for enabling intelligence and empathy within social robots is that of emotion recognition. In this paper, an automated and interactive computer vision system is investigated for human facial expression recognition and tracking based on the facial structure features and movement information. Twenty facial features are adopted since they are more informative and prominent for reducing the ambiguity during classification. An unsupervised learning algorithm, distributed locally linear embedding (DLLE), is introduced to recover the inherent properties of scattered data lying on a manifold embedded in high-dimensional input facial images. The selected persondependent facial expression images in a video are classified using the DLLE. In addition, facial expression motion energy is introduced to describe the facial muscle's tension during the expressions for person-independent tracking for personindependent recognition. This method takes advantage of the optical flow which tracks the feature points' movement information. Finally, experimental results show that our approach is able to separate different expressions successfully.

show abstract

Section: Nonlinear Dimension Reduction (Ndr) Methods For Person Depenmentioning

confidence: 99%

Facial expression recognition and tracking for intelligent human-robot interaction

Yang

Lee

et al. 2008

Intel Serv Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Extraction of data or features is a method to encode data with high accuracy. PCA is the main feature that extract by the system [15].…”

Section: Feature Reductionmentioning

confidence: 99%

Implementation of Hand Sign Recognition for Non-Linear Dimensionality Reduction based on PCA

Sharma¹,

Manchanda²

2017

IJIGSP

View full text Add to dashboard Cite

Abstract-HandSign or gesture recognition is the way of communication for hearing and speech impaired people. Gestures are formed from motion of body or state but commonly initiate from the hand and face. Speech and gestures are the expressions; these are the communication medium between human beings. Hand gesture is movement or motion of human hand. Gesture recognition is mathematical interpretation of human hand by using computing devices. There are different sign languages used in all over world and have its own grammar structure. Even in India has different languages used in every state, sign languages has little difference in contra dictionary region. Hand sign recognition is used for robot control applications and sign language interpretation.

show abstract

“…Hand gesture recognition requires solving a number of challenging computer vision and pattern recognition tasks, including (i) human skin segmentation [19,20] to extract hand regions from color images, (ii) hand pose estimation [8], (iii) hand tracking [10], and (iv) hand motion analysis and recognition [28,41]. Among the methods for estimating a hand pose, there are solutions based on localizing hand landmarks [6,17,45,51,54], extracting hand shape features [36,37,56], or fitting the parameters of a 3D hand model [15,49,59].…”

Section: Overview Of Vision-based Gesture Recognitionmentioning

confidence: 99%

Hand landmarks detection and localization in color images

Grzejszczak

Kawulok

Gałuszka

2015

Multimed Tools Appl

View full text Add to dashboard Cite

This paper introduces a new method for detecting and localizing hand landmarks in 2D color images. Location of the hand landmarks is an important source of information for recognizing hand gestures, effectively exploited in a number of recent methods which operate from the depth maps. However, this problem has not yet been satisfactorily solved for 2D color images. Here, we propose to analyze the skin-presence masks, as well as the directional image of a hand using the distance transform and template matching. This makes it possible to detect the landmarks located both at the contour and inside the hand masks. Moreover, we performed an extensive experimental study to compare the proposed method with a number of state-of-the-art algorithms. The obtained quantitative and qualitative results clearly indicate that our approach outperforms other methods, which may help improve the existing gesture recognition systems.

show abstract

Hand gesture recognition and tracking based on distributed locally linear embedding

Cited by 79 publications

References 27 publications

Facial expression recognition and tracking for intelligent human-robot interaction

Facial expression recognition and tracking for intelligent human-robot interaction

Implementation of Hand Sign Recognition for Non-Linear Dimensionality Reduction based on PCA

Hand landmarks detection and localization in color images

Contact Info

Product

Resources

About