Making gait recognition robust to speed changes using
mutual subspace method
Yumi Iwashita1 , Mafune Kakeshita2 , Hitoshi Sakano3 and Ryo Kurazume2
Abstract— Mutual subspace method (MSM), which is one ofimage-based approaches, showed strong discrimination capability in gait recognition. In general, 2D image matrices aretransformed into 1D image vectors to be used as input into
MSM, and then principal component analysis (PCA) is applied
to 1D vectors to generate a subspace. However, due to the highdimensionalities of 1D vectors, the evaluation accuracy of thecovariance matrix in PCA is not high enough. This results in adecrease in performance, especially in case that speed difference
between gallery and probe dataset is big. Thus in this paper we
propose a method, which expands the MSM-based method, torecognize people with higher accuracy. The proposed method
divides the human body area into multiple areas, followedby adaptive choice of areas that have high discrimination
capability. Moreover, the proposed method utilizes the frieze
pattern, which is one of gait features, as an additional input into
MSM. The use of divided areas and the frieze pattern allows
us to evaluate the covariance matrix with higher accuracy. In
experiments we applied the proposed method to challenging
databases with speed variations, and we show the effectivenessof the proposed method.

I. I NTRODUCTION
Gait is one of biometrics which do not require any
interaction with a subject and can be obtained from a
distance. Gait-based person recognition has been providing
new opportunities in various applications, such as surveillance system in public [6] and personalized robots which
provide adaptive service to people [11]. In general, gaitbased person identification methods extract features fromtime-series gait images, followed by person identification
based on extracted features. There are many existing methods
for feature extraction, such as gait energy image (GEI) [5],active energy image (AEI) [20] and frame difference frieze
pattern (FDFP) [16], which reported good performance withpublicly available gait databases [8] [1].
However, there are several causes which make the performance of gait recognition worse. Since appearance-based
method is sensitive to appearance changes, the performancecan be worse in case that a subject’s appearance is different
from that in the database. A possible situation is walking
speed change. The walking speed change causes variations
in pitch and stride, which result in appearance changes.
To address this problem, existing methods have focused ontransforming the gait features from various speeds into a
1 Yumi Iwashita is with Jet Propulsion Laboratory, California Institute ofTechnology, USA. This research was performed when she was at Kyushu
University. This paper does not contain any results from the researches at
Jet Propulsion Laboratory. Yumi.Iwashita@jpl.nasa.gov
2 Mafune Kakeshita and Ryo Kurazume with Kyushu University,
Fukuoka, Japan.
3 Hitoshi Sakano with NTT Data Corporation, Tokyo, Japan.

StaticStaticDynamic
Dynamic
Dynamic
2 km/h(a)

7 km/h(b)

Fig. 1. (a) Examples of gait images in OU-ISIR Treadmill Dataset A[13]. These images show that there are dynamic areas and static ones while
walking, (b) an example frieze pattern (F Ph ) of 7 km/h.

common walking speed or on extraction of speed-invariant
gait feature. Mansur et al. proposed a method [15] which
utilized a cylindrical manifold to synthesize constant-speed
gait images. Zeng et al. proposed a method for gait recognition based on silhouette features with deterministic learningtheory (DLT) [19]. Guan et al. proposed a classifier ensemble
method based on Random Subspace Method (RSM) concept[4]. Experimental results with OU-ISIR Treadmill Dataset A[13] by the RSM-based method showed the best performancecompared with other methods.
In [9] we proposed a new idea that speed information
does not have to be regarded as a critical information in
gait recognition but appearance information is important,since speed can change easily due to external factors, suchas crowded area and red traffic lights. Thus we regarded
that an image set-based matching approach could solve thegait recognition problem. To show the effectiveness of thisidea, we proposed a method [9] which applied a mutualsubspace method (MSM) [17] to gait images. Experimental
evaluations with a challenging gait database (OU-ISIR Gait
Speed Transition Dataset [15]) showed that the MSM-based
method [9] outperformed the state-of-the-art method [15].
We also evaluated the MSM-based method on anotherdataset (OU-ISIR Treadmill Dataset A [13], which wascollected on a treadmill with speed variations), and thoseresults are shown in experiments in this paper. In casethat speed variations are small between gallery and probe
dataset, the MSM-based method showed good performance.
However, in case that the speed variations are big, such as
fast walk (7 [km/h]) for gallery dataset and slow walk (2[km/h]) for probe dataset (Fig. 1 (a)), the performance wasworse than the RSM-based method [4]. We believe this could
be because of the following reason. As we can see in Fig. 1(a), there are dynamic areas and static areas while walking.
Even though there is speed difference between images, static
areas show similar appearance. On the other hand, dynamic

areas show different appearance. The MSM-based method[9] used entire area in each image as input into MSM, andthis causes decrease in performance because of the use of
dynamic area.
In general in MSM, a set of images for each class are
given as an input dataset into MSM and a model for eachclass is obtained as a subspace. To obtain the model, images
are transformed into 1D image vectors, which are highdimensional vectors, and then principal component analysis(PCA) is used to generate subspace from input vectors.
However, as Yang et al. [18] pointed out that the covariancematrix cannot be evaluated accurately due to its large size.
Moreover, transforming 2D images into 1D image vectors
results in reducing information of shape properties, due tothe loss of geometrical information.
To deal with these issues, we propose a method, which
expands the previous MSM-based method [9], to recognize
people more robust to speed variations than the previous
method. In the proposed method, the human body area ineach image is divided into multiple areas, and MSM [9] isapplied to each area. The idea of dividing area into small
multiple areas, which we proposed in [10], is integrated in
the MSM for the first time to the best of our knowledge.
A matching weight at each area is calculated automatically,
and areas which have higher discrimination capabilities are
adaptively selected based on the calculated weights. Then thesubject is identified by weighted integration of similarities of
all areas.
Basically the use of divided areas has an advantage thatit reduces the dimension of input vectors of MSM. This
results in higher accuracy of the evaluation of the covariancematrix than the use of full area. The same advantage canbe obtained by extracting gait features from images, which
represent gait images efficiently with smaller size of feature
vectors, rather than the direct use of image vectors as input
into MSM. There are existing methods to extract featuresfrom gait images [14] [20] [12] [7], and among them wefocus on frieze pattern [12] and affine moment invariants(AMIs) [7], which show high performance with small size offeature vector. In the proposed method we also utilizes gaitfeatures as input into MSM, in addition to image vectors.
Moreover, these two gait features describe shape properties.
Thus the use of gait features gives another advantage that it
incorporates shape properties into the covariance matrix.
Overall, the proposed method has three advantages: (i) the
extension of the MSM-based method by dividing the subject
area into multiple areas [10] for the purpose of high accuracyof the evaluation of the covariance matrix, (ii) the extensionof the MSM-based method using gait features (AMIs andfrieze patterns), and (iii) the evaluation of the MSM-based
method with two challenging gait databases, which consistof gait images with variety of speeds, such as OU-ISIR
Treadmill Dataset A [13] and CASIA-C Dataset [1]. We
experimentally confirm that the proposed method performs
better than existing methods.

II. M UTUAL SUBSPACE METHOD USING DIVIDED AREAS
FOR GAIT RECOGNITIONIn this section we briefly review MSM. Then, we introduce
a method to recognize people more robust to speed variations
than existing methods.
A. Mutual subspace method
MSM is regarded as one of powerful image set - image
set matching techniques. Let us assume C class patternrecognition problem. Bases of the class c gallery subspace
and input subspace are represented as φc and ψ, respectively.
Similarities in MSM are based on canonical angle θnc (n =1, . . . , N , and N is the number of canonical angles) between
two subspaces. More specifically, a similarity scn is a squareof a cosine of a canonical angle (i.e. cos2 θnc ), and similarities
are calculated as eigenvalues of the following matrix [2].
Z c = (ζij )c =M(φci · ψ m )(φcj · ψ m ).

(1)

m=1

In our method we use the maximum eigenvalue sc1 as thesimilarity between two subspaces. Class c is chosen, in casethat the maximum eigenvalue sc1 is the highest one among
all classes.
B. Proposed gait recognition method robust to speed variations
We explain the proposed method using divided areas, andwe introduce the use of gait features as input into MSM.
1) Mutual subspace method using divided areas: The
main steps of the gait recognition with MSM using divided
areas are as follows:
Step 1 We divided each image in the gallery dataset andthe probe dataset into K equal areas, according to
the height. Figure 1 (a) shows an example of ahuman body area divided into K =5 areas.
Step 2 We applied MSM to images at each area, and ateach area we calculated a similarity between each
person in the gallery dataset and a subject in theprobe dataset.
Step 3 We estimated a matching weight at each area according to the similarity. We set matching weights
high in areas with less appearance changes and low
in areas with appearance changes.
Step 4 Finally we identify the subject by weighted integration of similarities of all areas.
Details of Steps 2 to 4 are explained below.
In each of K divided areas a similarity sc1,k (k=1, . . . , K)
between gait images of the subject and those of class c in
the gallery dataset is calculated. Here, sc1,k is calculated as
the maximum eigenvalue from Eq. 1. Matching weights are
estimated as follows. In each area similarities between thesubject and all classes in the gallery dataset are sorted in adescending order, and a matching weight wkc of each classis given according to its resulting order. Similarities of allareas are integrated by
c

S =

K

k=1

wkc sc1,k .

(2)There are several ways to give the matching weights, and in
the proposed method we set 1.0 to the class which has thehighest similarity and 0.0 to the rest of classes. The subject
is identified as a person c in case that the final similarity S c
is the highest one among all classes.
The above process allows us not to utilize similarities
extracted from areas with low matching weights, which are
due to speed change, but it allows to utilize similarities fromareas with high matching weights. Therefore, the proposed
method enables person identification robust to speed variations. Moreover, dividing the subject area into multiple areas
makes a dimensionality of an input image vector into MSM
smaller compared with that of the original image vector,
and this allows the evaluation of the covariance matrix withhigher accuracy.
2) The use of image vectors and gait features: As we
explained in Section 1, there is an alternative way to makethe dimensionality of input vectors into MSM small, that is
the use of gait features extracted from images. There areseveral techniques to extract gait feature from each image.
In this paper we focus on frieze pattern [12] and AMIs [7],since these two techniques reported good performance. We
explain two methods briefly as follows.
Lee et al. proposed the frieze pattern [12], which has
two feature vectors 
defined as F Ph (y, t) =x I(x, y, t),
and F Pv (x, t) =y I(x, y, t). In this paper we used
F Ph (y, t) only, since the use of F Pv (x, t) caused a decreasein performance. Figure 1 (b) shows an example image offeature vectors of frieze pattern.
Affine moment invariants [3] are moment-based descriptors, which are invariant under a general affine transform.
The moments describe shape properties of an object as it
appears. There are totally 80 independent AMIs, which are
based on centralized moments μpq of order (p + q), and oneof 80 AMIs is defined as I1 = µ14 (μ20 μ02 − μ211 ).
00
The process of gait recognition using gait features isbasically the same with the one with image vectors as
follows. After images are divided into K areas, we extract K
feature vectors from each of gait images. Extracted feature
vectors from gait images at each area are used as input into
MSM, followed by calculation of matching weights in allareas. Matching weights are calculated in the same way withthe ones in Section II-B.1. Finally the subject is identified
by weighted integration of similarities of all areas.
We did preliminary experiments on OU-ISIR Treadmill
Dataset A [13] to compare the performance between thefrieze pattern and AMIs. We did matching between gait
images of 2 km/h (gallery) and those of 2 ∼ 7 km/h (every
1 km/h, probe). Table I shows correct classification ratiosof frieze pattern and AMIs, each of which is applied tothe proposed method. Parameters in the proposed method,which are the number of divided areas K and dimensionalities of subspaces of gallery and probe dataset, areselected with datasets for parameter training as we explain
in experiments. These results show the frieze pattern isbetter than AMIs. This could be because of the following
reason. From the definition of frieze pattern, we can say

TABLE IC OMPARISON OF C ORRECT C LASSIFICATION R ATIO (CCR) [%] OF
FRIEZE PATTERN [12] AND AMI S [7], EACH OF WHICH IS USED AS
INPUT INTO MSM. T HE SPEED OF GALLERY DATASET IS 2 KM / H , AND
THOSE OF PROBE DATASET IS EACH OF 2 ∼ 7 KM / H .

Friezepattern [12]AMIs [7]

2
100

3
100

100

100

Probe [km/h]
4
5
100 100
100

96

6
100

7
92

84

80

that each element of frieze pattern is correlated, since we
can assume that the human body shape is smooth. On theother hand, each element of AMIs is independent. Intuitively,
in case that correlations among feature elements are strong,feature vectors in each class can be represented with a lower
dimension subspace. This results in reducing the possibility
that subspaces among classes overlap each other, and thismay allow higher performance. Thus in the experiments, we
use the frieze pattern.
Since the frieze pattern represents shape properties, the useof frieze pattern allows to incorporate shape properties into
the covariance matrix. On the other hand, as we explainedin Section 1, the use of image vectors does not incorporate
shape properties into the covariance matrix. We can expect
the use of both image vectors and gait features may leadto higher performance than the use of each of them, sincethese two have different properties. Thus in our method weuse both as input into MSM.
III. E XPERIMENTS
In this section, we implement the proposed method andevaluate its performance on OU-ISIR Treadmill Dataset A[13] and CASIA-C Dataset [1]. The OU-ISIR Treadmill
Dataset A was collected on a treadmill with speed variations(from 2 km/h to 10 km/h, every 1 km/h). The CASIA-C
Dataset was collected at night using infrared cameras withseveral conditions including standard, speed, and carrying
variations.
A. Gait recognition with the OU-ISIR Treadmill Dataset A
The OU-ISIR Treadmill Dataset A consists of gait images
for 34 people, and from the dataset specification, 25 subjects
and the rest of subjects are assigned for evaluation andparameter training, respectively. In our method we haveparameters, i.e., the dimensionalities of subspaces of gallery
and probe dataset, and the number of divided areas K,
and these parameters are tuned with datasets for parameter
training.
This dataset consists of gait images with speed variations(from 2 km/h to 10 km/h, every 1 km/h)) for each gallery
and probe dataset, and subjects walked for speeds between
2 km/h to 7 km/h and run for speeds between 8 km/h to 10
km/h. In this section we used images for speeds between
2 km/h to 7 km/h, and we did experimental evaluations
with all combinations of gallery and probe speeds, i.e. 36

100
90
80
70
60
50
40
30
20
10
0

Correct classification ratio [%](Gallery vs Probe)
2 km/h vs 2 km/h
2 km/h vs 5 km/h
2 km/h vs 7 km/h

1

3

5

7

9

11

13

The number of divided areas K
Fig. 2. Correct classification ratio with respect to changes of the numberof divided areas K. We did matching between 2 km/h (gallery) and eachof 2, 5, and 7 km/h (probe).

combinations (”6 different speed for each gallery dataset” ×”6 different speed for each probe dataset”).
In the first experiments, we changed the parameter K to
see the performance changes with respect to the change ofK. We did matching between 2 km/h (gallery) and each of
2, 5, and 7 km/h (probe). We used image vectors as input
into MSM, and parameters of gallery and input subspaces are
tuned at each K. Figure 2 shows correct classification ratio(CCR) with K=1∼ 12. Here, the result of K=1 is equal to
the result of the MSM-based method [9]. From these results,
we can see that the proposed method which uses divided
areas is better than the MSM-based method [9], in case that
we choose proper parameters.
Table II show results of cross-speed walking people identification by the proposed method with image vectors, the proposed method with frieze pattern, and the proposed methodwith image vectors and frieze pattern. The use of both
image vectors and frieze pattern show the best performancecompared with the one of either image vector or frieze
pattern. Here, parameters of gallery and input subspaces andthe number of divided areas K are tuned simultaneously.
Please note that some of results in Table II are different fromthose in Table I, since the way how parameters are tuned isdifferent.
Moreover, we compared results with the RSM-based
method [4], which showed the best performance with thedataset, and results of [4] are shown in Table III. Our method
shows better performance than those of [4]. Table IV lists
the average classification ratios of the MSM-based method[9] (96.78 %), our proposed method (image vector, 97.89 %),our proposed method (frieze pattern, 98.78 %), our proposed
method (image vector and frieze pattern, 99.78 %), and theRSM-based method [4] (98.07 %). These results show theeffectiveness of the proposed method with image vector andfrieze pattern.
B. Evaluation of effectiveness of the matching weights
To evaluate the effectiveness of the matching weights wkc
in Eq. 2, which are set as 1.0 to the class with highest
similarity and as 0.0 to the rest of classes, we first checked
the performance at each area of K divided areas. Here, weused K=1∼3 as examples. Correct classification ratio foreach area between 2 km/h (gallery) and each of 2 ∼ 7km/h (probe) is shown in Table V. These results supports
the idea, which we mentioned in Section 1, that static
areas have higher discrimination capabilities than dynamic
areas. Interestingly, even though speeds of probe and gallery
datasets are the same (i.e. 2 km/h for both probe and gallery),the area ”3-3” has lower discrimination capability (28 [%]).
We checked a weight assigned to each area and Fig. 3
shows examples of assigned weights between gallery ID ”28”
and probe ID ”28” (K= 2 and 3). From these results, we cansee that higher weight (1.0) and lower weight (0.0) are set tostatic and dynamic areas, respectively. We confirmed that the
proposed method successfully assigns high weights to areas
which have higher discrimination capabilities.
We also did cross speed walking gait recognition with
wkc =1.0 for all classes (i.e. simple summation of similaritiesof all areas). Figure 4 shows results of matching between
2 km/h (gallery) and each of 2 ∼ 7 km/h (probe) by (a)proposed matching weights and (b) equal matching weights.
Here, we used image vectors and frieze pattern as input into
MSM. These results show that the use of proposed matchingweights improve the performance of gait recognition.
C. Gait recognition with the CASIA-C Dataset
In the last experiments we evaluated the proposed method(image vectors and frieze pattern) on the CASIA-C Dataset,
which consists of 153 subjects with 3 different walking
speeds and a carrying condition. Since this paper focuses
on walking speed condition, we apply the proposed method
to datasets of different walking speeds. Three walking conditions contain normal walking (f n), slow walking (f s), andfast walking (f q). For each subject, there are 4 sequences off n, 2 sequences of f s, and 2 sequences of f q. We used 3
normal walking (f n) sequences as the gallery set, and therest sequences were used as the probe set.
Table VI shows the results of the proposed method, theRSM-based method [4], the DLT-based method [19], and
AEI [20] for each of f n, f s, and f q. These results show theeffectiveness of the proposed method.
IV. C ONCLUSIONSThis paper described a method, which expanded the previous MSM-based method [9], to recognize people more robust
Correct classification ratio [%]
100
98
96
94
92
90
88
86
84
82
80

(a) Proposed wegiths(b) Equal weights
2

3

4
5
Probe [km/h]

67Fig. 4. Correct classification ratio between 2 km/h (gallery) and each of
2 ∼ 7 km/h (probe), with (a) proposed matching weights and (b) equalmatching weights wkc =1.0.

ProbeGallery
Weight for ID:28

Weight for ID:28

1.0

1.0

1.0
2 km/h

2 km/h

1.0

0.0

0.0

(1.0 assigned to
ID:32)(1.0 assigned to
ID:0)

4 km/h

7 km/h(a) 2 divided areas(a) 2 divided areas

2 km/hWeight for ID:28

Weight for ID:28

Weight for ID:28

Weight for ID:28

1.0

1.0

1.0

1.0

1.0

0.0
(1.0 assigned to
ID:32)

0.0

0.0

0.0

(1.0 assigned to
ID:8)(1.0 assigned to
ID:0)(1.0 assigned to
ID:5)

2 km/h

4 km/h

7 km/h(b) 3 divided areas(b) 3 divided areas

Fig. 3. Examples of weights assigned to areas of K(=2 and 3) divided areas. These results show weights between gallery ID ”28” and probe ID ”28”,
and numbers next to pictures show assigned weight.
TABLE IIC ORRECT CLASSIFICATION RATIO OF THE PROPOSED METHOD WITH ( I ) IMAGE VECTOR , ( II ) FRIEZE PATTERN , AND ( III ) IMAGE VECTOR AND FRIEZEPATTERN IN THE CROSS - SPEED WALKING GAIT RECOGNITION . R ESULTS ARE SHOWN AS ( I , II , III ).

XX

XXX Probe
XX
GalleryX
2 km/h
3 km/h
4 km/h
5 km/h
6 km/h
7 km/h

2 km/h

3 km/h

4 km/h

5 km/h

6 km/h

7 km/h(100, 100, 100)
(100, 100, 100)
(100, 100, 100)
(100, 100, 100)
(100, 100, 100)
(88, 84, 100)

(100, 100, 100)
(100, 100, 100)
(100, 100, 100)
(100, 100, 100)
(100, 100, 100)
(92, 92, 100)

(100, 100, 100)
(96, 100, 100)
(100, 100, 100)
(100, 100, 100)
(100, 100, 100)
(88, 100, 100)

(96, 100, 100)
(100, 100, 100)
(96, 100, 100)
(100, 100, 100)
(100, 100, 100)
(96, 100, 100)

(96, 100, 100)
(100, 100, 100)
(96, 100, 100)
(100, 100, 100)
(100, 100, 100)
(100, 100, 100)

(92, 88, 92)(92, 96, 100)
(96, 96, 100)
(96, 100, 100)
(100, 100, 100)
(100, 100, 100)

TABLE IIIC ORRECT CLASSIFICATION RATIO OF THE RSM- BASED METHOD [4] IN THE CROSS - SPEED WALKING GAIT RECOGNITION .

XXX
X

Probe
2 km/hXXX
GalleryX
2 km/h
100±0.00
3 km/h
100±0.00
4 km/h
100±0.00
5 km/h
92.8±1.69
6 km/h
92±0.00
7 km/h
92±0.00

3 km/h

4 km/h

5 km/h

6 km/h

7 km/h

100±0.00
100±0.00
100±0.00
96.4±1.26
94.4±2.07
94±2.11100±0.00
100±0.00
100±0.00
100±0.00
100±0.00
94.8±1.93

97.6±2.07
100±0.00
100±0.00
100±0.00
100±0.00
100±0.00

97.6±2.80
100±0.00
100±0.00
100±0.00
100±0.00
100±0.00

94±2.8398.4±2.07
90.4±2.80
96±0.00
100±0.00
100±0.00

TABLE IVC OMPARISON OF AVERAGE C ORRECT C LASSIFICATION R ATIO (CCR) [%] OF EACH MSM- BASED GAIT RECOGNITION [9], OUR PROPOSED METHOD( IMAGE VECTOR ), OUR PROPOSED METHOD ( FRIEZE PATTERN ), OUR PROPOSED METHOD ( IMAGE VECTOR AND FRIEZE PATTERN ), AND RSM- BASEDGAIT RECOGNITION [4] ON OU-ISIR T READMILL DATASET A [13].

CCR [%]

MSM-based gait
recognition [9]96.78

Our proposed method(image vector)
97.89

Our proposed method(frieze pattern)
98.78

to speed variations than the previous method. In the proposed
method, the human body area in each image was divided into
multiple areas, and MSM [9] was applied to each area. A
matching weight at each area was calculated automatically,
and areas which had higher discrimination capabilities wereadaptively selected based on the calculated weights. Then thesubject was identified by weighted integration of similarities

Our proposed method(image vector and frieze pattern)
99.78

RSM-based gait
recognition [4]98.07

of all areas. The proposed method has an advantage thatit can adaptively choose areas that have high discrimination
capability. Moreover, in the proposed method we utilized thefrieze pattern as an input into MSM. The use of divided areas
and the frieze pattern allowed us to evaluate the covariancematrix in MSM with higher accuracy, which resulted inhigher performance in gait recognition. We carried out exper-

TABLE V
C ORRECT CLASSIFICATION RATIO AT EACH AREA OF K DIVIDED AREAS (K=1 ∼ 3). T HE SPEED OF GALLERY DATASET IS 2 KM / H , AND THOSE OF
PROBE DATASET ARE 2 ∼ 7 KM / H . T HESE RESULTS SHOW THAT STATIC AREAS HAVE HIGHER DISCRIMINATION CAPABILITIES THAN DYNAMIC AREAS .
B OLD NUMBERS SHOW RELATIVELY HIGH PERFORMANCE .

The number ofdivided areas (K)Area

2 km/h

3 km/h

4 km/h

5 km/h

6 km/h

7 km/h

1

1-1

1-1

100

96

96

72

72

60

2-1

2-1

100

88

100

96

88

84

2-2

2-2

72

72

40

28

4

8

3-1

96

92

88

88

76

84

3-2

60

68

44

16

20

12

3-3

28

24

4

12

4

4

2

3-1
3-2
3-3

3

TABLE VIC OMPARISON OF AVERAGE C ORRECT C LASSIFICATION R ATIO (CCR) [%] OF EACH OF THE PROPOSED METHOD ( IMAGE VECTOR AND FRIEZEPATTERN ), RSM- BASED GAIT RECOGNITION [4], DLT- BASED GAIT RECOGNITION [19], AND AEI [20] ON CASIA-C [1]

fn
fs
fq

Our proposed method(image vector and frieze pattern)
100
99.7
99.7

RSM-based gait
recognition [4]10099.7±0.24
99.6±0.14

iments with two gait databases, and showed the robustness ofthe proposed method compared with conventional methods
against speed changes.
In this paper the proposed method was evaluated withdatasets of speed changes, but theoretically the method canwork with datasets of different variations such as clotheschanges. This is left as a future study.
ACKNOWLEDGMENT
The present study was supported in part by a Grant-in-Aid
for Scientific Research (A) (26249029).
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