Multimed Tools Appl
The new feature extraction method is demonstrated to be more efficient and effective
in the experiment section.
•
A more reliable gesture recognition method. We adopt the weighted KNN method for
the gesture recognition to replace the original genetic algorithm (GA) method in [29].
The new gesture recognition method achieves higher recognition rates, and therefore is
more reliable for real-world applications.
•
An easily implementable and extendable algorithm. The detailed system setup is
simple enough for starter level readers to implement an efficient and effective HCI
model, e.g. the simple five gestures user-defined database, the simple tortoise model
and the primitive W-KNN algorithm. Each step of the proposed method can be extended
to a more sophisticated approach for more complex applications.
2 Related works
Video-based hand gesture recognition methods, which extract hand gesture features from
video frames with or without model and later classify the features with supervised or
unsupervised learning technique, become incrementally popular in artificial intelligence,
signal processing, computer vision and virtual reality fields [8, 16, 21]. In 2007, Wang
[29] designed a simple tortoise model to recognize the basic human hand gestures. The tor-
toise model builds a feature space hand geometry and texture and is able to efficiently map
the target hand gesture with recognized hand gesture in database. The disadvantage of tor-
toise model is that the recognition result is highly sensitive to lighting, therefore requires
stable lightening environment. Yang et al. [35] proposed an static hand hand gesture recog-
nition system based on hand gesture feature space. The method did not handle the case
while the human face was overlapping with hands. The recognition accuracy rates dropped
quickly while the hand gesture differences were small. Zhang et al. [38] proposed a mean
shift dynamic deforming hand hand gesture tracking algorithm based on region growth.
This method did not require modelling for hand gestures but was highly sensitive to pre-
processing result. The recognition accuracy dropped quickly while the hand gestures change
drastically. Yao et al. [36] introduced a framework of hand posture estimation based on
RGB-D sensors. This method utilize the hand outlines to reduce the complexity of hand
posture mapping and support real-time complex hand gesture recognition. However, this
method was not able to handle hand gestures appearing with arm and body. The hand part
cannot be properly cut and recognized. Morency et al. [23] built a latent-dynamic discrim-
inative models (LDDM) to detect sequential hand gestures in a video file. Kurakin et al.
[11] introduced a real-time dynamic gesture recognition technique using a depth sensor.
The proposed method is efficient and robust to different gesture styles and orientations. Yin
et al. [37] introduced a high-performance training-free approach for hand gesture recogni-
tion using Hidden Markov Model (HMM) and Dynamic Time Warping (DTW). Recently,
Wu et al. [3, 31, 32] developed a dynamic gesture recognition system with the depth
information. The features of hand are extracted and the static hand posture are classified
using the support vector machine (SVM). Xie and Cao [33] presented an accelerometer-
based user-independent hand gesture recognition method. A simple database of 24 gestures
are used, including 8 basic gestures and 16 complex gestures. As a result, 25 features
are extracted based on the kinematics characteristics of the gestures, and treated as input