The document discusses performance evaluation of neural network-based hand gesture recognition. It begins with an abstract describing the study, which tested a proposed algorithm on 100 sign images from American Sign Language (ASL). The algorithm improved true match rate from 77.7% to 84% while decreasing false match rate from 8.33% to 7.4%. The introduction provides background on pattern matching versus recognition algorithms. The rest of the document details hand gesture recognition approaches, importance of gestures for human-computer interaction, basic architecture of a gesture recognition system including data acquisition, modeling, and feature extraction stages, and challenges in hand gesture recognition.

1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 3, March (2014), pp. 25-33 © IAEME
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PERFORMANCE EVALUATION OF NEURAL NETWORK BASED HAND
GESTURE RECOGINITION
Kavita Sharma1
, Dr. Anil Kumar Sharma2
1
M. Tech. Scholar, 2
Professor & Principal
Department of Electronics & Communication Engineering
Institute of Engineering & Technology, Alwar-301030 (Raj.), India
ABSTRACT
With the development of information technology in our Society one can expect that computer
systems to a larger extent will be embedded into our daily life. These environments lead to the new
types of human-computer interaction (HCI). The use of hand gestures provides an attractive
alternative to cumbersome interface devices for human-computer interaction (HCI). The existing
HCI techniques may become a bottleneck in the effective utilization of the available information
flow. Gestures are expressive, meaningful body motions. Interpretation of human gestures such as
hand movements or facial expressions, using mathematical algorithms is done using gesture
recognition. Gesture recognition is also important for developing alternative human-computer
interaction modalities. This research will have tested the proposed algorithm over 100 sign images of
ASL. The simulation will show that the true match rate is increased from 77.7% to 84% while the
false match rate is decreased from 8.33 % to 7.4%.
Keyword: ASL, Gesture Recognition, HCI, Neural Network, SIFT.
1. INTRODUCTION
Pattern recognition algorithms generally aim to provide a reasonable answer for all possible
inputs and to do "fuzzy" matching of inputs. This is different from pattern matching algorithms,
which looks for exact matches in the input with pre-existing patterns. Example of the pattern-
matching algorithm is regular expression matching, that looks for patterns of a given sort in textual
data and it is included in the search capabilities of many text editors and word processors. As
compared to pattern recognition, pattern matching is not considered as a type of machine learning,
although pattern-matching algorithms can sometimes succeed in providing similar-quality output to
the sort provided by pattern-recognition algorithms [1]. Computer is used by many people either at
their work or in their spare-time. Exceptional input and output devices have been designed over the
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years with the purpose of easing the communication between computers and humans, two most
known are the keyboard and mouse [1]. Each new device can be seen as an attempt to make the
computer more intelligent and making humans able to perform more complicated communication
with the computer. It has been possible due to the result-oriented efforts made by computer
professionals for creating successful human-computer interfaces [2]. The idea is to make computers
understand kind language and develop a user friendly human computer interfaces (HCI). Making a
computer understand speech, the facial expressions and human gestures are some steps towards it.
For human computer interaction (HCI) interpretation system there are two commonly approaches:
Data Gloves Approach and Vision Based Approach. The Data Gloves Approaches employ
mechanical or optical sensors. In gesture recognition, it is more common to use a camera in
combination with an image recognition system .These systems have the disadvantage that the
image/gesture recognition is very sensitive to illumination, hand position, hand orientation etc. In
order to circumnavigate these problems we decided to use a data glove as input device. Figure 1
shows the low cost design of hand gesture recognition system having data glove as input device. The
data glove makes the system independent to the hand orientation etc.
Fig. 1 Design of low Cost Data Glove Approach
The Vision Based Approaches techniques based on the how person realize information about
the environment. This technique uses the vision based properties like how object looks etc. The input
image may have same meaning but look different. The vision based methods are usually done by
capturing the input image using cameras. Figure 2 show the hand captured using the camera. The
interaction is based upon the vision properties.
Fig. 2 Vision Based Approaches
2. IMPORTANCE OF GESTURES
Gestures are an important part of everyday communication amongst humans. They
emphasize information in combination with speech or can substitute speech completely. They
can signify greetings, warnings or emotions or they can signal an enumeration, provide spatial

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information, etc [3]. Gestures are the non-verbally exchanged information. The person can perform
innumerable gestures at a time. Human gestures constitute a space of motion expressed by the body,
face, and/or hands. Among a variety of gestures, hand gesture is the most expressive and the most
frequently used. The Gestures have been used as an alternative form to communicate with computers
in an easy way. This kind of human-machine interfaces would allow a user to control a wide variety
of devices through hand gestures. A gesture is scientifically divided into two distinctive categories:
dynamic and static [24]. A dynamic gesture is intended to change over a period of time whereas a
static gesture is observed at the bounce of time. A waving hand means goodbye is an example of
dynamic gesture and the stop sign is an example of static gesture. Feelings and thoughts can also be
expressed by the gesture. Users generally use hand gestures for expression of their feelings and
notifications of their thoughts. The hand gesture and hand posture are two terms related to the human
hands in hand gesture recognition. Difference between hand gesture and hand posture, hand posture
is considered to be a static form of hand poses [4, 5]. Gestures can be classified into static gestures
and dynamic gestures. The static gestures are usually described in terms of hand shapes and dynamic
gestures are generally described according to hand movements. It can be defined as a meaningful
physical movement of the hands, fingers, arms [5], or other parts of the body with the purpose to
convey information or meaning for the environment interaction [4]. The Gesture recognition needs a
good interpretation of the hand movement as effectively meaningful commands [6].
3. HAND GESTURE RECOGNITION
Hand gesture recognition [7] is of great importance for human-computer interaction (HCI),
because of its extensive applications in virtual reality, the sign language recognition and computer
games. To enable more robust hand gesture recognition, one effective way is to use other sensors to
capture the hand gesture and motion, example: through the data glove. Unlike optical sensors, such
as sensors are usually more reliable and are not affected by lighting conditions or cluttered
backgrounds. But, as it requires the user to wear a data glove and sometimes requires calibration, this
is inconvenient for the user and may hinder the naturalness of hand gesture. Further, such data gloves
are expensive. For a result, it is not a very popular way for hand gesture recognition.
Fig. 3. Some Challenging Cases for Hand Gesture
Hand gesture has been used as a natural and efficient way in human-computer interaction.
Because of independence of auxiliary input devices, the vision-based hand interface is more
favorable for users. Still, the process of hand gesture recognition is very time consuming that often
brings much frustration to users.
Basic Architecture: The system of Gesture recognition consists of several stages; these stages are
varied according to application used, but, however, the unified outline can be settled, Figure 5 fulfils
this step.

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Fig.4. Basic Architecture of Gesture Recognition
The stages that are represented in latter figure might broke down into several sub-stages, all
these divisions are dependent on the application used, for example; the processing steps are different
in case of vision-based or glove-based were employed as well as the vision-base and color-based
hand gesture system.
• Data Acquisition: This is responsible for collecting the input data which are the hand
gestures image for vision based system or sensors reading in case of data glove, and the
classes should be declared that the classifier classifies the input tested gesture into one of
these predefined classes.
• Gesture Modeling: In this the fitting and fusing the input gesture into the model used, this
step may require some pre-processing steps to ensure the successful unification, and Figure 6
shows some common processing steps for successful fitting which will be briefed in the next
paragraph.
• Feature Extraction: The feature extraction should be smooth since the fitting is considered
the most difficult obstacles that may face; these features can be hand location, palm location,
fingertips location, and joint angles, the extracted features might be stored in the system at
training stage as templates or may be fused with some recognition devices such as neural
network, HMM, or decision trees which have some limited memory should not be overtaken
to remember the training data.
• Recognition Stage: This is the final stage for gesture system and the command/meaning of
the gesture should be declared and carried out, this stage usually has a classifier that can
attach each input testing gesture to one of predefined classes with some likelihood of its
matching to this class.
4. SCALE INVARIANT FEATURE TRANSFORM (SIFT) & NEURAL NETWORK
Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and
describe local features in images. For any object in an image, interesting points on the object can be
extracted to provide a "feature description" of the object. This description which is extracted from a
training image, used to identify the object when attempting to locate the object in a test image
containing many other objects [9]. To perform reliable recognition, the features extracted from the
training image are detectable even under changes in image scale, noise and illumination. These
points usually lie on high-contrast regions of the image, like an object edges. Neural networks (NN)
are composed of simple elements operating in parallel. These are inspired by biological nervous

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systems. Network function is determined largely by the connections between elements. They trained
a neural network to perform a particular function by adjusting the values of the connections
(weights) between elements [10]. Commonly neural networks are trained, so specific input leads to a
specific target output. Such a situation is shown in fig. 3. There, the network is adjusted, based on a
comparison of the output and the target, until the output of the network matches the target. Many
input/target pairs are used, in this supervised learning to train the network.
Fig.5. Neural Net Block Diagram [10]
5. PROPOSED ALGORITHM
In the existing technique the distance ratio and the threshold is taken fixed i.e. 0.65, 0.035.
These values of distance ratio and the threshold get incremented and decremented respectively by the
value 0.05. But these values of the distance ratio and the threshold makes perfect match to be
avoided. The NN is used to decide the value of the distance ratio and the threshold. Here the
supervised learning is used to train the network. The input to the NN is the input image and the
desired signal is the corresponding database image. The error gives distance between the input and
target signal. The error is minimized to get the threshold value. The ratio of difference between the
input and the target at this value is taken as the distance ratio value. The proposed algorithm is
divided in two phases i.e. the training phase and recognition phase. In the training phase the neural
network is trained to detect the value of the distance ratio and the threshold. In the recognition phase
the gesture is recognized by the sift point matching algorithm. Following algorithm explains the
whole process.
Training Phase
1. Input the sample Image.
2. Preprocess the image
3. Take Corresponding database image as the target signal.
4. Use feed backward neural network with the specified input in step 1 & step 2.
5. The minimum error is taken as the threshold value.
6. The ratio at this value is taken is taken as the distance ratio value.
Recognition Phase
7. Run the SHIFT match algorithm.
8. Key point matching starts its execution by running the threshold.
9. Key points are matched between test and all the trained images.
10. The key point matching defines the validity ratio.
11. The image having proper validity ratio displayed as the result.

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6. RESULTS
The software used for Simulation is MATLAB 7.0. Fig 6, 7 & 8 shows various output of the
program.
Fig. 6. Found Match using Proposed Algorithm
Fig.7. Match found using Proposed Algorithm
Fig.8. False Match using Existing Algorithm

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We applied several operations on image then matched the image with the database image.
Tabe-1 shows the recognition after operations.
Table 1: Result of Manipulated images
Operation Image Maximum Key
Point in Sample
Image
Number of
Keypoint
Matched
Rotate 90 253 220
Rotate 180 242 211
Rotate 270 242 211
Flip
Horizontal
232 200
lip Vertical 233 208

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Table-2 shows the number of key points matched as well as the maximum number of key
points in the sample image for all alphabets. The match column denotes that whether resultant image
is true match or false match or not found.
Table 2: Results for 26 Characters
7. CONCLUSION
The gesturing is rooted in our life, and the human continues this behavior during hhis talk
with others even those others were on the phone; this form of communication is appealing since
it provides the basic and easy effort done by a person to get the message delivered rather than the
cumbersome devices like keyboard and mouse since the former reflects his nature. For that
reason, the keyboard and mouse have been replaced by hand, face, finger, or entire body and
those new natural input devices attracting more attention since need no extra connections, more
efficient, powerful, promising, and preferable naturalness. In this research, we have tested the
proposed algorithm over 100 sign images of ASL. If the image is present in database and the
algorithm detects the same image, then it is the true match. If an algorithm recognizes the wrong
image, then it is known as the false match. The simulation shows that the true match rate is
increased from 77.7% to 84% while the false match rate is decreased from 8.33 % to 7.4%.
Hence the proposed algorithm improves the performance.

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