We present a fuzzy-based decision embedded in in rule-based methods that spontaneously changes pixel intensity for every frame in a given by evaluating both hue type and level of intensity prior to the feature extraction step. The term fuzzy is to answer such question as “How low or high is the frame brightness that is categorized as bright or dark type of frame?” and vice versa. In comparison to normal background subtraction and hard-based decision, the designed fuzzy mechanism intents to demonstrate an enhancement or unravel the visibility of illumination discrepancy which has been video analysis’s nemesis. Thus, we illustrate the development in post-processing phase via applying the results onto gait recognition task. The following results are measured and compared by percentage value of Correct Classification Rate (CCR) for each approach. More than one thousands of hand-held recorded videos and static surveillance videos were acquired as experiment samples.

1.
A.
Ibrahim
,
W.
Mohd-Isa
and
C.
Ho
, “
Gait silhouette extraction from videos containing illumination variates
,” in
9th Int. Conf. on Robotic, Vision, Signal Processing and Power Applications Lecture Notes in Electrical Engineering
398
, edited by
H.
Ibrahim
, et al
(
Springer Singapore
,
2016
), pp.
229
236
2.
X.
Yin
,
B.
Wang
,
W.
Li
,
Y.
Liu
and
M.
Zhang
. “
Background subtraction for moving cameras based on trajectory-controlled segmentation and label inference
,” in
KSII Trans. on Internet and Information Systems
9
(
10
), pp.
4092
4107
(
2015
).
3.
H.
Wang
and
D.
Suter
. “
A re-evaluation of mixture of Gaussian background modeling
,” in
Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing
2
, pp.
1017
1020
(
2005
).
4.
Z.
Zivkovic
and
F.
Van Der Heijden
. “
Efficient adaptive density estimation per image pixel for the task of background subtraction
,” in
Pattern Recognition Letters
27
(
7
), pp.
773
780
(
2006
).
5.
M.
Hoffman
and
G.
Rigoll
, “
Exploiting gradient histograms for gait-based person identification
,” in
Proc. IEEE Int. Conf. on Image Processing (ICIP)
, pp.
4171
4175
(
2013
).
6.
M.
Xu
and
T.
Ellis
, “
Illumination-invariant motion detection using color mixture models
,” in
Proc. Brit. Mach. Vis. Conf.
, pp.
163
172
(
2001
).
7.
H.
Ng
,
W.
Tan
and
J.
Abdullah
, “
Multi-view gait based human identification system with covariate analysis
,” in
The Int. Arab J. of Information Technology
10
(
5
), pp.
519
526
(
2013
).
8.
X.
Tan
and
W.
Triggs
, “
Enhanced local texture feature sets for face recognition under difficult lighting conditions
,” in
IEEE Trans. on Image Processing
19
(
6
), pp.
1635
1650
(
2010
)
9.
N. M.
Fitzgerald
, “
Human identification via gait recognition using accelerometer gyro forces
,”
Yale Computer Science
, http://www.cs.yale.edu/homes/mfn3/pub/mfn_gait_id.pdf (accessed November 12, 2015).
10.
F.
Tafazzoli
,
G.
Bebis
,
S.
Louis
and
M.
Hussain
, “
Improving human gait recognition using feature selection
,” in
Advances in Visual Computing. ISVC 2014 Lecture Notes in Computer Science
888
, edited by
G.
Bebis
, et al
(
Springer
Cham
,
2014
), pp.
830
840
.
11.
H.
Zhang
,
A. C.
Berg
,
M.
Maine
and
J.
Malik
, “
SVM-KNN: discriminative nearest neighbor classification for visual category recognition
,” in
Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition
2
, pp.
2126
2136
(
2006
).
12.
R.
Gutierez-Osuna
, “Introduction to pattern recognition,” in
Pattern Recognition and Intelligent Sensor Machine
(
Texas A&M University
,
2010
).
13.
D.
Menotti
,
L.
Najman
,
J.
Facon
and
A. A.
De Araujo
, “
Multi-histogram equalization methods for contrast enhancement and brightness preserving
,” in
IEEE Trans. Consumer Electron.
53
(
3
), pp.
1186
1194
(
2007
).
14.
H.
Ibrahim
and
N. S. P.
Kong
, “
Brightness preserving dynamic histogram equalization for image contrast enhancement
,” in
IEEE Trans. Consumer Electron.
53
(
4
), pp.
1752
1758
(
2007
).
15.
S. S.
Agaian
,
B.
Silver
and
K. A.
Panetta
, “
Transform coefficient histogram-based image enhancement algorithms using contrast entropy
,” in
IEEE Trans. Image Process.
16
(
3
), pp.
741
758
(
2007
).
16.
M.
Kaur
, “
K-nearest neighbor classification approach for face and fingerprint at feature level fusion
,” in
Int. J. of Computer Applications
60
(
14
), pp.
13
17
(
2012
).
This content is only available via PDF.