 Open Access
 Authors : Jixi Gu
 Paper ID : IJERTV13IS100090
 Volume & Issue : Volume 13, Issue 10 (October 2024)
 Published (First Online): 28102024
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Sign Language Recognition using Feature Selection Based on Binary Blackwinged Kite Optimization Algorithm
Jixi Gu
Nanjing No.1 Middle School International Department Nanjing, China
AbstractSign language recognition aims to help understand sign language and bridge the communication gap between speech or hearingimpaired and other nonimpaired individuals. Sign language recognition belongs to the area of pattern recognition. Feature selection is an important basic approach for solving the pattern recognition problem, and it reduces and refines the feature set of data so that the generated feature subset can further improve the pattern recognition accuracy. In this paper, a new binary blackwinged kit optimization algorithmbased feature selection method, BBKAFS, is proposed. When applying to sign language recognition, BBKAFS can not only reduce the feature dimensions, but also obtain higher recognition accuracies on three public sign language data sets.
Keywordssign language recognition; feature selection; swarm intelligence; pattern recognition; blackwinged kit algorithm

INTRODUCTION
Sign language is different from oral language, and it is a sophisticated manual language employing hand gestures, body movements, and facial expressions to express individual feelings, thoughts, intentions, needs, and so on[1]. Therefore, sign language becomes a dominant tool of connection between speech or hearingimpaired communities and the general people. The understanding of sign language routinely depends on visual inputs, and automatic sign language recognition can use pattern recognition techniques to realize the translation of this visualgestural language into the normal spoken or written language[2]. The higher the sign language recognition accuracy is, the smoother the communication between deaf and dumb people and the social world will be.
In the era of big data and artificial intelligence, pattern recognition algorithms or models require to have the faster learning speed, the stronger generalization capability, the better recognition accuracy, and so on. Feature Selection, which is often regarded as one of basic techniques related to the area of pattern recognition, can take advantage of some evaluation criteria to create the new feature subset based on original data. When recognition algorithms or models are applied to the new generated feature subset of data, the better performance will be achieved. Feature selection methods are broadly categorized into three kinds: embedded methods, wrapper methods, and filter methods[3]. Wrapper methods refine the feature subset by the operational results of one classification algorithm[4], and in this paper, the proposed approach based on binary swarm intelligence optimization is classified as this type.
Swarm intelligence optimization is a class of optimization methods, and it can search for the global solutions to problems by mimicking and modelling the behavioral characteristics of one group, and its algorithms not only are employed flexibly, but also are easy to be understood. Popular swarm intelligence optimization algorithms include particle swarm optimization[5], grey wolf optimization[6], whale optimization algorithm[7], Harris Hawk Optimization(HHO)[8], Black Kite optimization Algorithm (BKA)[9], and so on. It is worth noting that BKA is a latest metaheuristic optimization algorithm inspired by the migration and attack behaviors of blackwinged kites, and it exhibits the satisfactory performance in complex functions and engineering cases. Since feature selection is a binary discrete problem in fact, at present, many binary swarm intelligence optimization algorithms are commonly used in the wrapper based feature selection method, such as binary particle swarm optimization[10], binary grey wolf optimization[11] and Binary HHO(BHHO)[12]. Like BBHO based Feature Selection (BBHOFS) in [12], these binary versions of methods consider the classification error rate of a learning model as the optimization objective, and can help to search for and obtain better feature subsets in a discrete space.
In this paper, first we design a Binary BKA(BBKA), which employs the Vtype function to transform continuous space into discrete space, and next present a new BBKA based Feature Selection method(BBKAFS), and finally apply this proposed BBKAFS to sign language recognition. Experimental results on three public sign language data sets demonstrate that when compared to other approaches, BBKAFS can not only find a feature subset with smaller size, but also acquire higher recognition accuracies.

BINARY BLACKWINGED KITE ALGORITHM (BBKA)
BBKA based on the currently presented BKA[9] that mimics and models the predatory and migration behavior of black winged kites mainly consists of three stages as follows:

Initialization Stage
, xij , , xid
Assume that X is a pack of n blackwinged kites, and let X x1, x2, , xi , , xn where xi is the position of the ith blackwinged kite and each xi xi1 , xi 2
where xij represents the jth dimension of the position of the ith
blackwinged kite and d is the number of dimensions of the position space. Each xij can be initialized as follows:
, and the curve image of this Vshaped function is shown in
Fig.1. Furthermore, note that r1 takes the different value for different i and different j .
xij rand int(0,1)
(1)
Last, after the completion of the continuous to binary
where rand int(0,1) generates a random number of 0 or 1. Assume that f is the optimization object of BBKA, that is,
conversion,
function. If
xi and
x
b
i
f x
f
b should be evaluated by the fitness
x
i
, then let x xb ; otherwise x
the fitness function.
f xi is used to evaluate the position of
i i i i
remains unchanged.
the ith blackwinged kite, and a kite located at the best position
xbest
is considered as the leader of kites. The position xbest is
defined as:
xbest arg min( f (xi ))
xi , xi X
(2)
, and
f (xbest ) is the optimal value of the fitness function.

Attacking Stage
When hunting small prey in the air, the blackwinged kite needs to frequently adjust its position. First, to mimic this behavior, a mathematical model of updating the position xi of the ith blackwinged kite is defined as:
c xi (sin( ) 1) xi
xi
x
i
xi (2 1) xi
(3)
where
c is the new generated position of the ith black
Fig.1. The curve image of the Vtype function
winged kite in the attacking stage, like the original BKA, is a random number between 0 and 1, 0.9 , and is gotten
as follows:
q 2

Migration Stage
First, like BKA, a hypothetical migration behavior of the blackwinged kites is mimicked and a related mathematical model is defined as:
2 Q
x w (x x ) f (x ) f (x )
0.05 e
(4)
xc
i i best i g
(7)
i x w (x h x ) f (x ) f (x )
where q is the current iteration number, and Q is the i best i i g
maximum number of iterations. Moreover, note that takes the different value for different i .
Second, BBKA asks to search the optimal position in the 0 1 binary space, but all new positions generated by Eq.(3) are in the continuous space. Therefore, a continuous to binary map is needed to fulfil the space conversion, and it is defined as:
x r1 V (xc )
where c is the new generated position of the ith black
x
i
winged kite in the migration stage, xbest is the position of the blackwinged kites leader and is currently optimal, g is a random integer with the range of 1, n , and w and h can be respectively computed as:
xb
ij ij
(5)
h 2sin( )
(8)
ij ij
ij x r1 V (xc )
2
and
where
xij
the jth dimension of the position xi
that is not
w tan((r2 0.5) )
(9)
updated by Eq.(3), xc is the jth dimension of the positions xc where takes the same value as of Eq.(3), w is a Cauchy
ij
in the continuous space,
i
x
ij
b is the jth dimension of the
random number related to Cauchy distribution, and r2 is a random number between 0 and 1. Besides, note that g takes
positions
xb in the binary space, x
is the complement of
the different values for different i , and
r2 also takes the
i ij
2
xij , r1 is a random number between 0 and 1, V () is a V type transfer function defined as:
distinct values for distinct i .
Second, a continuous to binary space conversion is still employed in the migration stage, and a map similar to Eq.(5) is defined as:
V (t)
arctan(
t)
(6)
x r3 V (xc )
2 xb
ij ij
(10)
ij ij
ij x r3 V (xc )
i
where
xij
the jth dimension of the position xi
that is not
Step 6: For xb , perform KNN and get the classification error
updated by Eq.(7) after the attacking stage,
c is the jth
rate on TrnFS and ValFS , and calculate the fitness
x
ij
dimension of the positions xc in the continuous space, xb is
function value f xb . If f
, then let
i ij
x
b
i
f x
the jth dimension of the positions xb in the binary space,
i i
x xb ; otherwise x remains unchanged.
i i i i
xij is the complement of xij , r3 is a random number between 0 and 1, and V () is Eq.(6). Additionally, note that
Step 7: Set the random numbers g , r2 and r3 , update xi and get xc by Eq.(7), and convert xc to xb by Eq.(10).
i i i
x
b
i
f x
i
r3 takes the different value for different i and different j . Last, after the completion of the continuous to binary
conversion, x and xb should be also evaluated by the fitness
Step 8: For xb , perform KNN and get the classification error rate on TrnFS and ValFS , and calculate the fitness
x
b
i
f x
i i
value
f xb . If
f
, then let x
xb ;
function. If
f
, then let x
xb ; otherwise x
i
i i i
i
i i i
remains unchanged.
otherwise xi remains unchanged.


BBKABASED FEATURE SELECTION (BBKAFS)
Step 9: Set i i 1. If i n , then goto Step 5.
Step 10: Sort all f xi ( i 1, 2, , n ), and acquire
xbest
BBKAbased feature selection, that is, BBKAFS, belongs to the wrapper method, and for it, this paper designs a fitness function f related to the classification error rate obtained
from a simple classification algorithm, that is, KNearest Neighbors (KNN). Assume that each xi ( i {1, 2, , n}) is viewed as a candidate feature subset, and this fitness function f [13] is defined as:
f x (1 ) (x ) (xi ) (11)
by Eq.(2) again.
Step 11: Set q q 1. If q Q , then goto Step 4; otherwise BBKAFS ends, and xbest is the finally attained optimal feature subset.

Sign Language Recognition Experiments

Experimental Settings and Data Sets
First, since the essence of sign language recognition is regarded
i i
max
as the classification problem of sign language, and in order to
measure the performance of the above proposed BBKAFS, we
where (xi ) is the classification error rate on the data with the feature subset xi , max is the size of the original whole feature set, (xi ) is the size of the feature subset xi , and
0.01.
The detailed procedure for BBKAFS is given as follows:
Step 1: Let n be the number of candidate feature subsets, and
Q be the maximum number of iterations. Set q 1 ,
0.9 , and 0.01.
Step 2: Let TrnFS and ValFS respectively be the training and validation data specially used for feature selection,
X be a set of all feature subsets, and each xi ( i {1, 2, , n} ) in X be a candidate feature subset.
Step 3: Initialize each xi by Eq.(1). For each xi , carry out classification of KNN and obtain the classification error rate (xi ) on TrnFS and ValFS , and calculate the fitness function value f xi by Eq.(11). Sort all f xi ( i 1, 2, , n ), and acquire xbest by Eq.(2).
Step 4: Set i 1.
Step 5: Set the random numbers and r1, update xi and get
xc by Eq.(3), and convert xc to xb by Eq.(5).
employed KNN for the data classification and the combination with the wrapperbased feature selection, and we also compared BBKAFSbased KNN with BBHOFSbased KNN and KNN without feature selection.
Second, the parameter K of KNN classifier was set to 5, For BBHOFS and BBKAFS, the number n of candidate feature
subsets was set to 10, the maximum number Q of iterations was set to 100, Eq.(11) was used as the fitness function, and other parameters of BBHOFS was set according to [12].
Next, three sign language data sets from Kaggle[14] were used
in experiments. There three data sets were as follows: British Sign Numbers(BSN), German Sign Language Alphabet(GSLA) and Leap Motion American Sign Language(LMASL). Each data set was divided into the training and testing sets with the approximate ratio of 8:2. KNN, BBHOFSbased KNN and BBKAFSbased KNN were performed and evaluated on these training and testing data. Moreover, for each data set, this training data that accounts for 80% of the total data was again split into TrnFS and ValFS in the approximate ratio of 5:5, and TrnFS and ValFS were specially used for the feature selection of BBHOFS and BBKAFS. Table I lists main characteristics of three sign language data sets, and Table II gives the number of samples in the testing data, the training data, TrnFS and ValFS for each data set.
Finally, experiments were done by Matlab on MacOS running on the personal computer with Inter Core i7 and 16GB
i i i
table i. Sign Language Data Sets Used in
Experiments
Data set
The number of samples
The size of the feature set
The number of classes
BSN
1194
63
11
GSLA
7306
63
24
LMASL
5145
428
18
2058
Data set
Testing data
Training data
TrnFS
ValFS
BSN
239
955
477
478
GSLA
1461
5845
2922
2923
LMASL
1029
4116
2058
TABLE II. THE NUMBER OF SAMPLES IN THE TESTING, TRAINING, TRNFS AND VALFS DATA
table iii. Comparison of The Sizes of Optimal Feature Subsets
Data set
No feature selection
BBHOFS
BBKAFS
BSN
63
30
7
GSLA
63
32
10
LMASL
428
207
29
Fig.2. Comparison of classification accuracies on BSN
Fig.3. Comparison of classification accuracies on GSLA
Fig.4. Comparison of classification accuracies on LMASL
RAM. All experimental results were averaged on 20 runs for each data set.

Experimenal Results
First, the comparison of the sizes of optimal feature subsets generated by BBHOFS and BBKAFS is reported in Table III. As shown in Table III, on the one hand, when no feature selection is employed, the optimal feature subset is the original entire feature set, and on the other hand, BBKAFS can acquire the lower size of the optimal feature subset than BBHOFS on each sign language data set.
Second, the classification accuracies on three sign language data sets are respectively shown in Fig.2, Fig.3 and Fig.4. In Fig.2 and Fig.3, over 90% classification accuracies are attained only by BBKAFSbased KNN, and in Fig.4, although there is little different classification performance between BBHOFS based KNN and BBKAFSbased KNN, the latter, that is, the proposed BBKAFSbased KNN, still can achieve the highest accuracy. Furthermore, Fig.2, Fig.3 and Fig.4 describe that when compared with feature selection based KNN, such as BBHOFSbased and BBKAFSbased KNN, the simple KNN without feature selection underperforms on three sign language data sets.


CONCLUSIONS
To improve the accuracy of sign language recognition, this paper proposes an effective blackwinged kite optimization algorithm based feature selection method, that is, BBKAFS. Compared to other approaches for sign language recognition, our method (i) enables to refine and get the optimal feature subset with the smaller size, and (ii) has the better sign language recognition performance.
REFERENCES

S. Alyami, H. Luqman, and M. Hammoudeh, Reviewing 25 years of continuous sign language recognition research: advances, challenges, and prospects, Information Processing & Management, vol.61, No.5, pp.103774, September 2024.

Y.Q. Zhang, X.W. Jiang, Recent advances on deep learning for sign language recognition, Computer Modeling in Engineering & Sciences, vol.139, no.3, pp.23992450, March 2024.

A. Moslemi, A tutorialbased survey on feature selection: recent advancements on feature selection, Engineering Applications of Artificial Intelligence, vol.126, part D, pp.107136, November 2023.

B.H. Nguyen, B. Xue, and M.J. Zhang, A survey on swarm intelligence approaches to feature selection in data mining,Swarm and Evolutionary Computation, vol.54, pp.100663, May 2020.

A.G. Gad, Particle swarm optimization algorithm and its applications: a systematic review, Archives of Computational Methods in Engineering, vol.29, pp.25312561, April 2022.

S. Mirjalili, S.M. Mirjalili, and A. Lewis, Grey wolf optimizer,
Advances in Engineering Software, vol.69, pp.4661, March 2014.

S. Mirjalili, A. Lewis, The whale optimization algorithm, Advances in
Engineering Software, vol.95, pp.5167, May 2016.

A.A. Heidari, S.Mirjalili, H. Faris, and et al., Harris hawks optimization: algorithm and applications, Future Generation Computer Systems, vol.97, pp.849872, August 2019.

J. Wang, W.C. Wang, X.X. Hu, and et al., Blackwinged kite algorithm: a natureinspired metaheuristic for solving benchmark functions and engineering problems, Artifical Intelligence Rewiew, vol.57, March 2024.

J.W. Too, A.R. Abdullah, and N.M. Saad, A new coevolution binary particle swarm optimization with multiple inertia weight strategy for feature selection, Informatics, vol.6, no.2, May 2019.

J.W. Too, A.R. Abdullah, N.M. Saad, and et al., A new competitive binary grey wolf optimizer to solve the feature selection problem in EMG signals classification, Computers, vol.7, no.4, November 2018.

J.W. Too, A.R. Abdullah, and N.M. Saad, A new quadratic binary harris hawk optimization for feature selection, Electronics, vol.8, no.10, October 2019.

T. Dokeroglu, A. Deniz, H.E. Kiziloz, A comprehensive survey on recent metaheuristics for feature selection, Neurocomputing, vol.494, no.14, pp.269296, July 2022.

Kaggle datasets website, https://www.kaggle.com/datasets.