Person Re-identification using
Co-occurrence Attributes of Physical andAdhered Human Characteristics
Masashi Nishiyama, Shota Nakano, Tappei Yotsumoto, Hiroki Yoshimura,
Yoshio Iwai and Kazunori Sugahara
Graduate School of Engineering, Tottori University, Japan
Email: nishiyama@eecs.tottori-u.ac.jp
Abstract—We propose a novel method for extracting featuresfrom images of people using co-occurrence attributes, which arethen used for person re-identification. Existing methods extract
features based on simple attributes such as gender, age, hairstyle, or clothing. Our method instead extracts more informative
features using co-occurrence attributes, which are combinationsof physical and adhered human characteristics (e.g., a man
wearing a suit, 20-something woman, or long hair and wearing
a skirt). Our co-occurrence attributes were designed using priorknowledge of methods used by public websites that search forpeople. Our method first trains co-occurrence attribute classifiers.
Given an input image of a person, we generate a feature by vectorizing confidences estimated using the classifiers and computea distance between input and reference vectors with a metriclearning technique. Our experiments using a number of publicly
available datasets show that our method substantially improved
the matching performance of the person re-identification results,
when compared with existing methods. We also demonstrated
how to analyze the most important co-occurrence attributes.

I.

I NTRODUCTION

Person re-identification is an active topic in pattern recognition research and has many potential applications such as
watchlist monitoring and video surveillance. The problem is
especially difficult because we must allow for variable viewpoints, illuminations, and poses. To overcome these difficulties,
researchers have developed various approaches for extracting
invariant features from images of people. These features havesignificant influences on the matching performance. In thispaper, we focus on extracting invariant features that represent
the people in images.
There are currently two main approaches for extracting
features to be used in person re-identification applications.
The first exploits low-level features [1]–[4] such as the distributions of gradients and colors. The second exploits highlevel representations [5]–[8] such as gender, age, clothing,and gait, which are called human attributes in the field ofsoft-biometrics. However, it is exceptionally difficult to accurately infer high-level representations. Instead of inferring each
attribute, researchers have recently started to use mid-level
semantic attributes [9], [10] for person re-identification. These
attributes directly represent elements of features extracted fromimages of people. However, existing methods for determining
invariant features are not sufficient for person re-identification,
when compared with characteristics that are used when people
identify each other.

	Physical	
  and	
  adhered	  	
  human	
  characteris1cs	  Person re-identification	
using	
co-occurrence attributes	

	

&	

&	

Physical	
  and	
  adhered	  	
  human	
  characteris1cs	
  Fig. 1. Person re-identification using co-occurrence attributes. We propose
using combinations of physical and human characteristics to extract featuresfrom images of people.

Our key idea is to exploit our prior knowledge of thecharacteristics used by people when looking for others. For
instance, public websites of open criminal investigations [11],[12] or missing persons [13], [14] use a combination of physical and human characteristics such as gender and clothing.
These combinations are considered to be informative clues
when searching for people, and we expect that they would
be useful invariant features for person re-identification.
In this paper, we attempt to determine what kind of combinations of physical and human characteristics are valuablefor person re-identification, as illustrated in Figure 1. We callthese combinations co-occurrence attributes. We propose anovel method for automatically extracting features based on
the confidences of co-occurrence attributes, to enhance theperformance of person re-identification methods. The extracted
features were developed using our prior knowledge of thecharacteristics used in public websites that identify or findpeople. The rest of this paper is organized as follows. Section II
contains a brief summary of related work, and Section III
describes the design of the co-occurrence attributes. Section IV
describes the proposed method for extracting features using
co-occurrence attributes. Section V contains the results ofour experiments and analyses, which demonstrate the methods
effectiveness. Our concluding remarks are given in Section VI.
II.

R ELATED W ORK

Existing methods exploit low-level features such as histograms of oriented gradients [2], color histograms [1], or their
combinations [3]. The learning-based approach in [4] selected
effective low-level features from filter banks of gradients andcolors. Unfortunately, low-level features are often affectedWoman	
wearing a skirt	

Man in his	20s	Wearing 	short sleeves and shorts	

Co-­‐occurrence	
  (physical	
  &	
  adhered)	
  Co-­‐occurrence	
  (physical	
  &	
  physical)	
  Co-­‐occurrence	
  (adhered	
  &	
  adhered)	
  Man	
wearing	long sleeves	Man	
wearing	short sleeves	Man	
wearing	a suit	

&	

&	

&	

Woman	
wearing	long sleeves	

Woman	
wearing	short sleeves	

Woman	
wearing	a suit	

&	

&	

&	

Fig. 2. Examples of co-occurrence attributes of physical and adhered humancharacteristics.

by the variability of viewpoints, illuminations, and poses.
To extract invariant features, researchers have focused onhuman attributes [15]. We consider each characteristic (e.g.,gender, hair style, or clothing) as a single attribute. Somemethods [16], [17] improved the inference of single attributes
using a joint learning technique. Khamis et al. [6] proposed
jointly optimizing the inference of single attributes and theidentification. Shi et al. [9] applied a domain shift between
fashion and surveillance data to avoid complications due tolabeling processes. Layne et al. [10] developed a method
for weighting single attributes to increase the identification
performance. However, existing methods have not sufficiently
considered the combinations of physical and adhered humancharacteristics that are often used when people identify eachother.
III.

D ESIGN OF CO - OCCURRENCE ATTRIBUTES

Fig. 3. Binary labels for representing co-occurrence attributes of physical(gender) and adhered human (tops) characteristics.

label to represent a co-occurrence attribute by assuming thateach characteristic class is independent. For instance, whencombining gender (L1 = 2) and tops (L2 = 3), we obtain
L1 L2 = 6 binary labels for the co-occurrence attributes.
As illustrated in Figure 3, binary labels are represented as“man wearing long sleeves”, “man wearing short sleeves”,“man wearing a suit”, “woman wearing long sleeves”, “woman
wearing short sleeves”, and “woman wearing a suit” (andhave true or false attributes). In biometrics, [18] showedthat binary representations are intuitively easy for humans
to understand, when compared with discrete and continuous
representations. By exploiting the binary representation of cooccurrence attributes, our aim is to easily analyze which cooccurrence attributes are useful for person re-identification
methods.

A. Combinations of physical and human characteristics
We designed co-occurrence attributes that can be extracted
from images of people. As described in [18], human attributes
can be split into three intuitive types: physical, behavioral,
and adhered human characteristics. Physical characteristics are
person-specific traits and do not significantly change over time(e.g., gender, age, hairstyle, and beard). Behavioral characteristics are temporal changes such as gesture or gait. Adhered
human characteristics depend on a persons appearance and aredefined as things that are temporarily attached to a person (e.g.,clothing or sunglasses).
We used combinations of physical and adhered humancharacteristics because we are motivated by certain public
websites [11]–[14]. There are three ways to combine two
characteristics, as illustrated in Figure 2: a combination ofphysical and adhered human characteristics (e.g., “ woman
wearing a skirt”), a combination of physical characteristics(e.g., “man in his 20s”); and a combination of adhered humancharacteristics (e.g., “wearing short sleeves and shorts”).
B. Binary representation
Physical and adhered human characteristics use two typesof class labels: binary labels such as gender (male or female)and sunglasses (presence or absence); and multi-class labels
such as age (e.g., 20s, 30s, or over 40) and tops (e.g., long
sleeves, short sleeves, or suit jacket). When simply combining
two characteristics with L1 and L2 classes, the number ofclasses in the combination is L1 L2 . Our method uses a binaryIV.

P ERSON RE - IDENTIFICATION USING CO - OCCURRENCEATTRIBUTES

A. Overview of our method
We now briefly describe our method that uses cooccurrence attributes for person re-identification, which isillustrated in Figure 4. We train co-occurrence attribute classifiers to infer whether an image of a person contains theattributes. In this training process, we use images that havebeen labeled with co-occurrence attributes. These images are
assigned positive or negative labels following the binary representation described in Section III-B. Note that this design
complicates the labeling process, because there are many
combinations of physical and adhered human characteristics.
Thus, we automatically assigned co-occurrence attributes tothe training samples using combinations of the labels for
single attributes. If we have L1 L2 combinations of single
attributes, there is 1 positive label of a co-occurrence attributeand L1 L2 − 1 negative labels. For instance, a positive label
in Figure 3 is “man wearing long sleeves”; the remaining are
negative.
Given an input image of a person, we compute the confidences of co-occurrence attributes using the trained classifiers.
We generate a feature vector for person re-identification by
vectorizing the confidences, and compute the distance between
an input feature vector and a reference feature vector. The
details of each step are described below.

	

&	

…	

&	

	

1. Train classifiers	
True examples

	Classiﬁer	
  of	
  	
  
co-­‐occurrence	
  a1ribute	
  1

Conﬁdence	
  1Classiﬁer	
  of	
  	
  
co-­‐occurrence	
  a1ribute	
  2

Conﬁdence	
  2

…

…

Classiﬁer	
  of	
  	
  
co-­‐occurrence	
  a1ribute	
  N	
  Conﬁdence	
  N	
  

…	
False examples

2. Estimate confidences	

Feature	
  vector
Metric	  learning	
  Feature	
  vector
3. Compute a distanceFig. 4. Overview of our method that uses co-occurrence attributes for person
re-identification.

B. Training classifiers of co-occurrence attributes
There may be much less positive attributes than negative.
This is particularly the case when using co-occurrence attributes. If the number of positive and negative samples are
significantly different, generic machine learning classification
algorithms do not work well. To overcome the problems
associated with imbalanced data, we can weight attributes
according to inverse of the number of samples or align thenumber of samples. The alignment approach performed thebest in our preliminary experiments, so this is the method weused in the remainder of this paper.
Next, we extracted feature vectors from the images to train
the classifiers of co-occurrence attributes using the ensemble oflocalized features (ELF) method [4]. We used a linear support
vector machine classifier SVMi (i ∈ 1, ..., N ), where N is thenumber of co-occurrence attributes.
C. Extracting features for person re-identification
We describe how to compute the features used to match
images of people. Given an image, we compute a signed
distance hi by applying a co-occurrence attribute classifier,that is,hi = SVMi (e) ,(1)where e represents an ELF vector extracted from an imageof a person. An image is assigned a positive label when thesigned distance is positive, and vice versa. We regard a signed
distance as a confidence value, in the same manner as existing
techniques. Note that if we directly use the signed distances as
elements of a feature vector, the ranges of possible values are
imbalanced because they depend on their respective element.
Thus, we apply a simple scaling technique to align the rangesof values. We estimate the confidence value xi = 2h0i −1 usingh0i =hi − min hti,
max hti − min hti(2)where max or min returns the maximum or minimum valuehti (t ∈ 1, ..., T ), and T is sum of the number of positive and negative samples. The feature vector for person reidentification is x = [x1 , x2 , ..., xN ]T .
D. Computing the distance between feature vectors
We describe how to compute the distance between feature
vectors extracted from images of people. If we use the all

elements of a feature vector, we believe that some elements
contribute to the identification and others do not. To increase
the matching performance, some methods give larger weights
to the important elements of a feature vector using a greedy
algorithm technique [10] or a metric learning technique [19].
Our method used the large margin nearest neighbor (LMNN)
method [20]. This technique generates a metric matrix M that
reduces the distance between feature vectors belonging to thesame person using the k-nearest neighbors, while lengthening the distance between vectors from different people. The
distance, d, between features xa , xb is
d2 = (xa − xb )T M(xa − xb ) .

(3)

Note that d is smaller when the feature vector of individual ais more similar to the feature vector of individual b.
V. E XPERIMENTAL ANALYSIS OF PERSON
RE - IDENTIFICATION USING CO - OCCURRENCE ATTRIBUTES
We evaluated the effectiveness of our method using somecomputational experiments. The person re-identification results
are reported in Section V-A and our analysis of a metricmatrix trained using LMNN is given in Section V-B. We
also evaluated our method on a number of public datasets,
as described in Section V-C.
A. Evaluation of basic performance1) Experimental conditions: To evaluate the performanceof our person re-identification method, we used the PETA
Dataset [21], which consists of 10 publicly available datasets:
3DPeS [22], CAVIAR4REID [23], CUHK [24], GRID [25],i-LID [26], MIT [27], PRID [28], SARC3D [29], TownCentre [30], and VIPeR [31]. In the PETA dataset, all theimages are labeled with single attributes. We selected 15
single attributes, as shown in Table I. P represents physicalcharacteristics and A represents adhered human characteristics.
In our experiments, we selected single attributes from [10]and public websites [11]–[14]. Note that we did not selectcolor attributes, because colors significantly vary between
surveillance cameras (as described in [18]).
We also designed 96 co-occurrence attributes, as shown in
Table II. P&A represents combinations of physical and adhered human characteristics, P&P represents combinations ofphysical characteristics, and A&A represents combinations ofadhered human characteristics. We removed 23 combinationsof single attributes (e.g., “wearing a suit jacket and shorts”)
from the 119 combinations, because they did not commonlyoccur in practical cases. There were 59 P&A attributes, 22
P&P attributes, and 15 A&A attributes (Table II). The numberof positive samples for each co-occurrence attribute was 48 orabove.
We split the PETA dataset into test and training samples. We used images of people from VIPeR to evaluate
the performance of the person re-identification method. We
randomly selected 316 individuals from VIPeR for our test
sample. The remaining 316 individuals were used to train ametric matrix for LMNN. We repeated the random selection
10 times to generate different test sample sets. We used
two major indicators: cumulative match characteristic (CMC)
curves dependent on the n-th rank matching rate, and theTABLE I.
S INGLE ATTRIBUTES FOR EVALUATING AN EXISTINGMETHOD . W E USED 2 CLASSIFIERS FOR BINARY CHARACTERISTICS AND
13 CLASSIFIERS FOR MULTI - CLASS CHARACTERISTICS .

Age 31–45 & Shorts (P&A)

Male & Long Hair (P&P)

Multi-class label

Suit Jacket & Long Sleeves (A&A)

Age 46–60 & Short Sleeves (P&A)

Gender(Male/Female)Hairstyle(Short/Long/Bald)
Age(16–30/31–45/46–60/Over 60)

Female &Age Above 60 (P&P)

Male & Age16–30 (P&P)

Short Hair & Suit Pants (P&A)

Bald & Short Sleeves (P&A)

Short Hair & Short Sleeves (P&A)

Female & Skirt (P&A)

Sunglasses & Short Hair (P&A)

Age31–45 & Shor tHair (P&P)

Tops(Short Sleeves/Long Sleeves/Suit)
Bottoms(Shorts/Skirt/Suit)

Age 46–60 & Suit Jacket (P&A)

Male & Long Sleeves (P&A)

Female & Suit Pants (P&A)

No Sunglasses & Shorts (A&A)

Sunglasses & Short Sleeves (A&A)

Age 46–60 & Bald (P&P)

Long Sleeves & Suit Pants (A&A)

No Sunglasses & Long Sleeves (A&A)

Age 31–45 & Long Hair (P&P)

Female & Age 31–45 (P&P)

Female & Suit Jacket (P&A)

Age 31–45 & Suit Pants (P&A)

Age 16–30 & Short Sleeves (P&A)

Female & Long Sleeves (P&A)

	
  	
  CA
	
  	
  (91.6)

Short Hair & Skirt (P&A)

Male & Age Above 60 (P&P)

Male & Suit Jacket (P&A)

Age 16–30 & Suit Jacket (P&A)

	
  	
  SA
	
  	
  (85.1)

Age 46–60 & Long Hair (P&P)

Male & Short Hair (P&P)

Female & Shorts (P&A)

Shorts & Short Sleeves (A&A)

	
  	
  ELF
	
  	
  (77.8)

Age 46–60 & Long Sleeves (P&A)

Sunglasses & Female (P&A)

Female & Age 46–60 (P&P)

Long Hair & Shorts (P&A)

Age 16–30 & Suit Pants (P&A)

Female & Short Hair (P&P)

No Sunglasses & Suit Pants (A&A)

Long Hair & Suit Pants (P&A)

Female & Age 16–30 (P&P)

Suit Jacket & Short Sleeves (A&A)

No Sunglasses & Suit Jacket (A&A)

Age 31–45 & Long Sleeves (P&A)

Skirt & Long Sleeves (A&A)

Sunglasses & Age 31–45 (P&A)

Age 16–30 & Shorts (P&A)

Age 16–30 & Short Hair (P&P)

Sunglasses & Long Sleeves (A&A)

Long Hair & Skirt (P&A)

Male & Short Sleeves (P&A)

Age 46– 60 & Short Hair (P&P)

Long Hair & Long Sleeves (P&A)

Age Above 60 & Short Sleeves (P&A)

Bald & Long Sleeves (P&A)

Age 31–45 & Short Sleeves (P&A)

Male & Suit Pants (P&A)

Female & Short Sleeves (P&A)

No Sunglasses & Age 46–60 (P&A)

Female & Long Hair (P&P)

Sunglasses & Male (P&A)

Suit Jacket & Suit Pants (A&A)

SA: An existing method [10] that extracts featuresusing singles attributes. Note that we used the single
attributes in Table I and LMNN instead of a greedy
algorithm.

Age Above 60 & Bald (P&P)

No Sunglasses & Female (P&A)

No Sunglasses & Skirt (A&A)

Male & Age 31–45 (P&P)

Age Above 60 & Suit Jacket (P&A)

Age 46–60 & Suit Pants (P&A)

Short Hair & Suit Jacket (P&A)

Long Hair & Short Sleeves (P&A)

No Sunglasses & Age 31–45 (P&A)

Sunglasses & Age 16–30 (P&A)

ELF: An existing method [4] that extracts low-levelfeatures using filter banks of gradients and colors.

Age Above 60 & Long Hair (P&P)

No Sunglasses & Age 16–30 (P&A)

Age 31-45 & Skirt (P&A)

Long Hair & Suit Jacket (P&A)

No Sunglasses & Short Hair (P&A)

Male & Age 46–60 (P&P)

Male & Bald (P&P)

No Sunglasses & Short Sleeves (A&A)

Male & Shorts (P&A)

Age 16–30 & Skirt (P&A)

Sunglasses & Long Hair (P&A)

No Sunglasses & Age Above 60 (P&A)

No Sunglasses & Bald (P&A)

Age 31–45 & Suit Jacket (P&A)

No Sunglasses & Long Hair (P&A)

Age Above 60 & Long Sleeves (P&A)

Age 16–30 & Long Hair (P&P)

No Sunglasses & Male (P&A)

Age 16–30 & Long Sleeves (P&A)

Short Hair & Long Sleeves (P&A)

Shorts & Long Sleeves (A&A)

Short Hair & Shorts (P&A)

Adheredhumancharacteristics(A)

Sunglasses(Presence/Absence)

Matching	
  rate	
  (%)

100

80
60
40
20
0

1

Rank

316

Fig. 5.
CMC curve evaluated on the VIPeR dataset. The number inparentheses represents the nAUC for each method.

normalized area under the CMC curve (nAUC). We compared
the following methods.

••

C O - OCCURRENCE ATTRIBUTES FOR EVALUATING OURMETHOD .

Binary label
Physicalcharacteristics(P)•

TABLE II.

CA: Our method for extracting features from images
using the co-occurrence attributes described in Section IV.

2) Experimental results: Figure 5 shows the performanceof the person re-identification results based on the CMC curves
and nAUC. The plot shows the average values for the 10 setsof randomly generated test samples. We can clearly see that
our features based on the co-occurrence attributes are superior
to features using singles attributes and the low-level features
based on gradient and color. Overall, our method outperformed
the others, and improved the matching rate for rank n = 20
by 20 points when applied to this difficult task.
To determine the most effective combinations of characteristics, we applied the method•

P1: without the combinations of physical and adhered
human characteristics (P&A);•

P2: without the combinations of physical characteristics (P&P);•

P3: without the combinations of adhered human characteristics (A&A); and•P4: using all the combinations in Table II.

Table III shows the matching rates and nAUCs for thesefour experiments. The combination of physical and adhered
human characteristics increased the performance of the person
re-identification method (the results for P1 were inferior tothe others). The method using all the combinations (P4)produced the best results. These results demonstrate thatthe co-occurrence attributes of physical and adhered humancharacteristics developed using our method can successfully
re-identify people in images.

n=1

n=5

n = 10n = 20

nAUC

P1

7.9

24.5

37.4

54.1

89.2

P2

9.9

29.5

42.1

58.6

90.9

P3

11.6

30.6

44.0

60.5

91.4

P4

11.3

32.0

45.6

63.9

91.6

Cumulative contribution ratio	

Plan

1.0	

0.5	

0.0	
1	
15	
No. eigenvalues of single attributes 	
1.0	

Cumulative weight ratio	

Cumulative weight ratio	

Cumulative contribution ratio	

TABLE III.
C OMPARISON OF MATCHING RATES AND NAUC TO
EVALUATE THE EFFECTIVENESS OF THE DIFFERENT COMBINATIONS .

0.5	

0.0	
1	
15	
No. elements of the 1st eigenvector of	
single attributes 	

1.0	

0.5	

0.0	
1	
96	
No. eigenvalues of co-occurrence attributes 	Co-occurrence attributes
2nd eigenvector

Age 31–45 & Shorts (P&A)

Short Hair & Shorts (P&A)

Suit Jacket & Long Sleeves (A&A)

Female & Age 31–45 (P&P)

Short Hair & Suit Pants (P&A)

No Sunglasses & Age 31–45 (P&A)

Short Hair & Short Sleeves (P&A)

Male & Age 46–60 (P&P)

Age 46–60 & Suit Jacket (P&A)

Age 31–45 & Shorts (P&A)

Female & Suit Pants (P&A)

Male & Suit Pants (P&A)

Long Sleeves & Suit Pants (A&A)

Age 16–30 & Long Hair (P&P)

Age 31–45 & Long Hair (P&P)

Bald & Short Sleeves (P&A)

Co-occurrence attributes
3rd eigenvector

Single attribute1st eigenvector

Age 16–30 & Suit Jacket (P&A)

Short Hair (P)Female & Skirt (P&A)

1.0	

No Sunglasses & Suit Jacket (A&A)
Male & Age Above 60 (P&P)
Female & Age Above 60 (P&P)

0.5	

Age 31–45 & Short Sleeves (P&A)
0.0	
1	
96	
No. elements of the 1st eigenvector of	
co-occurrence attributes 	B. Analysis of the metric matrix of LMNN
1) Analysis algorithm: To determine the most valuableco-occurrence attributes, we investigated a metric matrix M
trained using LMNN. The matrix M is positive-semidefinite
and is represented asNX

C OMPARING THE IMPORTANCE OF ELEMENTS WHENCOMPUTING THE DISTANCE .

Co-occurrence attributes
1st eigenvector

Fig. 6. Comparing the cumulative contribution ratio and the cumulative
weight ratio curves between co-occurrence and single attributes.

M=

TABLE IV.

λi q i q i T ,(4)2) Analysis results: We analyzed the matrix of cooccurrence attributes (MCA ) and the matrix of single attributes (MSA ). We investigated the elements of eigenvectors
under the condition that the cumulative contribution ratios
and cumulative weight ratios of MCA and MSA were nearly
identical. Both matrices were trained using images of 632
individuals from the VIPeR dataset. We selected eigenvectors
corresponding to the eigenvalues when Cm = 0.4: the 1st, 2nd
and 3rd eigenvectors of MCA and the 1st eigenvector of MSA .
We selected elements of the eigenvectors for Wi,n = 0.2:
eight elements of the 1st eigenvector of MCA , eight elementsof the 2nd eigenvector of MCA , six elements of the 3rd
eigenvector of MCA , and one element of the 1st eigenvectorof MSA . Figure 6 shows the cumulative contribution ratio andthe cumulative weight ratio curves of MCA and MSA .

i=1

where λi is the i-th eigenvalue and q i is the i-th eigenvector.
The eigenvectors corresponding to larger eigenvalues have themost important role when computing the distance between
feature vectors (Equation (3)). We compute the cumulative
contribution ratio Cm from λ1 to λm using
Cm =

m
X
k=1

λk
PN

i=1 λi.

(5)

To determine the impact of the l-th element qi,l in eigenvector q i when computing the distance between feature vectors,
we compute the weight ratio wi,l , i.e.,|qi,l |

wi,l = PN

j=1 |qi,j |Table IV shows the informative elements of the eigenvectors of MCA and MSA . There were 12 co-occurrence attributes
containing age and seven co-occurrence attributes containing
gender. Age and gender are indeed important for person reidentification, because these physical characteristics are also
used in public websites [11]–[14]. There were six, three, and13 co-occurrence P&P, A&A, and P&A attributes in Table IV,
respectively. Three co-occurrence attributes contained short
hair. These results show that an effective characteristic appears
in both co-occurrence and single attributes. We believe that our
method can effectively determine the characteristics used whenpeople recognize each other.
C. Evaluation using public datasets,(6)and compute the cumulative weight ratio Wi,l from wi,1 towi,l , i.e.,l
XWi,l =
wi,k .
(7)k=1

Note that wi,k (k ∈ 1, ..., l) are in descending order.

We evaluated the performance of our method using the
3DPeS, CAVIAR4REID, GRID, i-LID, PRID, SARC3D, andTownCentre datasets. We used test samples for person reidentification, training samples for LMNN, and training samples for the attribute classifiers with the experimental conditions described in Section V-A1. Table V shows the nth matching rates and nAUC for the person re-identification
results. We can see that our method using co-occurrence
attributes is superior to the existing method [10] using single

TABLE V.
C OMPARING MATCHING RATE AND NAUC OF THE PERSON
RE - IDENTIFICATION RESULTS USING A NUMBER OF PUBLIC DATASETS .

[7]

DatasetMethodn=1

n=5

n = 10n = 20

nAUC

3DPeS

CA
SA

20.0
14.1

39.8
31.2

51.0
42.2

65.0
56.9

78.6
74.2

CAVIAR
4REID

CA
SA

24.7
15.6

55.8
40.7

72.3
59.2

90.1
80.2

81.7
72.9

GRID

CA
SA

18.9
10.1

46.4
30.9

65.1
45.2

80.3
62.5

89.9
81.9

[10]

i-LID

CA
SA

20.9
10.7

46
32.8

60.7
48.4

77.6
68.3

81.2
73.7

[11]

PRID

CA
SA

8.9
4.1

28.5
19.5

44.2
31.4

61.3
50.6

78.7
71.3

[12]

SARC3D

CA
SA

60.9
50.0

94.5
90.3

98.9
98.4

99.5
99.9

96.5
94.7

[13]

Town
Centre

CA
SA

32.3
18.4

49.9
35.1

57.8
44.2

67.6
55.1

82.5
74.6

[14]

[8]
[9]

[15]

attributes, on the all datasets. This significant improvement in
the matching performance demonstrates the effectiveness ofthe co-occurrence attributes of combinations of physical andadhered human characteristics.
VI.

C ONCLUSION

We proposed a method for person re-identification thatuses co-occurrence attributes. To extract invariant features fromimages of people, we introduced a combination of physical andadhered human characteristics. The contributions of this work
can be summarized as follows.
••

We designed co-occurrence attributes using physicaland adhered human characteristics, which were basedon prior knowledge of how people recognize eachother.
We analyzed a metric matrix trained using a metriclearning technique to determine the most important
co-occurrence attributes.

In the future, we will extend our method by considering
combinations of three or more characteristics. We also intend
to combine low-level features with our method.

[16]

[17]

[18]

[19]

[20]

[21]
[22]

[23]

[24]

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