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Fashion Analysis: CurrentTechniques and Future Directions
D riven by the huge profit potential in the
fashion industry, intelligent fashion anal-
ysis based on techniques for clothing and make-
over analysis is receiving a great deal of
attention in the multimedia and computer
vision literature. In China, total retail sales in
the clothing market exceeded $977.8 billion
yuan in 2012, and its apparel market is one of
the “fastest growing markets in the world.”1
Such a huge market prospect greatly motivates
clothing relevant research. Many cosmetic
companies in the world, such as Gucci and
L’Or�eal, have also seen huge economic gains.
L’Or�eal reported that the world cosmetic mar-
ket grew from 2005 to 2012, and the company’s
operating profit reached 22 million Euros in
2012.2
As Figure 1 shows, fashion analysis contains
two components: clothing analysis and facial
beauty (including makeup and hairstyle) analy-
sis. This article surveys the state of the art in
both areas. We first provide an overview of the
techniques for clothing modeling3 and then
introduce the representative works on clothing
recognition.4 Based on the results of clothing
recognition, clothing parsing can be conducted.
(The first clothing parsing work was conducted
by Kota Yamaguchi and his colleagues.5 Later,
several researchers proposed different methods
for clothing parsing with various inputs.6)
These clothing-related techniques serve as the
foundation for two clothing-related applica-
tions: clothing retrieval and recommendation.
Our survey also provides several example imple-
mentation systems from this field of research,
including the Street-to-Shop system7 and the
Magic Closet.8
In facial beauty research, some research
works focus on the internal facial regions (such
as facial shape, eyes, and mouth). Others focus
on external facial regions, such as hair. We first
survey several research directions related to
facial and hair beauty. Then, we introduce the
Beauty e-Experts system, which is a complete
system with both automatic makeover recom-
mendation and synthesis.
Clothing Analysis TechniquesThere is already a large body of research on
clothing modeling, recognition, parsing,
retrieval, and recommendations. As a brief
overview, here we introduce the representative
works of each category.
Clothing Modeling
Hong Chen and his colleagues presented a con-
text-sensitive grammar in an AND-OR graph
representation that can produce a large set of
composite graphical templates to account for
the variability of cloth configurations.3 In the
supervised learning phase, an artist is asked to
draw clothing sketches that are decomposed
into body components. Each component has a
number of distinct subtemplates. These sub-
templates serve as leaf nodes in an AND-OR
graph. The AND-nodes represent a decomposi-
tion of the graph into subconfigurations with
Markov relations for context and constraints,
and the OR-nodes are switches for choosing
one out of a set of alternative AND-nodes.
The work done by Chen and his colleagues
mainly involves constructing a 2D model of the
clothing.3 Several researchers have also pro-
posed methods for estimating a 3D clothing
model and utilized it to estimate the 3D shape
of a person from images of that person wearing
clothing.
Clothing Recognition
Ming Yang and Kai Yu presented a complete sys-
tem for tagging clothing categories in real time.4
Specifically, the system takes advantage of face
detection and tracking to locate human figures,
and the authors also developed an efficient
clothing segmentation method utilizing Voro-
noi images to select seeds for region growing.
Si Liu, Luoqi Liu,and
Shuicheng YanNational
University ofSingapore
Cees SnoekUniversity of AmsterdamMedia Impact
1070-986X/14/$31.00�c 2014 IEEE Published by the IEEE Computer Society72Authorized licensed use limited to: BEIHANG UNIVERSITY. Downloaded on April 01,2020 at 13:49:50 UTC from IEEE Xplore. Restrictions apply.
Other researchers follow almost the same
pipeline for recognizing and classifying human
clothing. The main difference is the use of dif-
ferent kinds of classifiers, such as random forest.
Clothing Parsing
Kota Yamaguchi and his colleagues developed an
effective method for parsing clothing in fashion
photographs, which is an extremely challenging
problem because of the large number of possible
garment items and the variations in configura-
tion, garment appearance, layering, and occlu-
sion.5 In addition, to enable future research on
clothing parsing, they provided a large novel
dataset and tools for labeling garment items.
Later, Yamaguchi and his colleagues further
tackled the clothing parsing problem using a
retrieval-based approach. For a query image,
they found similar styles from a large database
of tagged fashion images and used these exam-
ples to parse the query. Their approach com-
bines parsing from pretrained global clothing
models, local clothing models learned on the
fly from retrieved examples, and transferred
parse masks (paper doll item transfer) from
retrieved examples.
Jian Dong and his colleagues used parselets
as the building blocks of a parsing model.6 Par-
selets are a group of parsable segments that can
generally be obtained by low-level over-seg-
mentation algorithms. They built a deformable
mixture parsing model (DMPM) for human
parsing to simultaneously handle the deforma-
tion and multimodalities of parselets. Their
model has two unique characteristics: the possi-
ble modalities of parselet ensembles are exhib-
ited as the AND-OR structure of subtrees and
the visibility properties are directly modeled at
some leaf nodes.
Clothing Retrieval
The early works on clothing retrieval mainly
tackled this problem by leveraging low-level
features and high-level attributes of clothes.
Typical clothing retrieval systems may utilize a
content-based image retrieval approach first, in
which a codebook is constructed from extracted
dominant color patches. A reranking approach
is then used to improve the search quality by
exploiting clothing attributes.
Si Liu and her colleagues addressed the practi-
cal problem of cross-scenario clothing retrieval:
given a photo of a person captured in a general
environment, such as on the street, they
attempted to find similar clothing in online
shops, where the photos were captured more
professionally and with clean backgrounds.7 In
this case, there are large discrepancies between
the daily photo scenario and the online shop-
ping scenario. The authors proposed a solution
that includes two key components: human/
clothing parts alignment to handle human pose
variations and bridging cross-scenario discrep-
ancies with an auxiliary daily photo dataset.
Figure 2 shows their proposed framework.
First, given a daily photo, 20 upper body parts
and 10 lower body parts are located. Second,
each human part in the daily photo, such as the
Fashionanalysis
(1) Recommendation
Dating
(2) Retrieval
(3) Parsing
Clothing Makeover
(1) Attractiveness prediction
(2) Makeup synthesis
(3) Makeover recommendation
More attractive Less attractive
Figure 1. Research on fashion analysis mainly studies two aspects: clothing and makeovers. Clothing
analysis tasks include clothing recommendation, retrieval, and parsing, whereas makeover analysis tasks
involve attractiveness prediction, makeup synthesis, and makeover recommendations.
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zoomed-in right shoulder, y in Figure 2, is line-
arly reconstructed by the collected auxiliary set
Y with a sparse coefficient a. Because both y and
Y are daily photos, the precision of the online
within-scenario reconstruction can be guaran-
teed. The auxiliary set Y is collaboratively
reconstructed offline by the OS dataset X, and
the sparse reconstruction coefficient matrix Z is
obtained by multitask sparse representation
with the constraint that the reconstruction
errors corresponding to the same spatial posi-
tion (several rows of the error term E) are acti-
vated or turned off simultaneously.
Finally, the feature representation of the
daily photo y is refined by integrating the
online calculated within-scenario sparse coeffi-
cient a and the offline cross-scenario collabora-
tive reconstruction coefficient Z by y0 ¼ XYa.Consequently, a nearest-neighbor search of the
OS dataset can be implemented based on the
reconstructed new feature representation y0.
Clothing Recommendation
Few existing works target the clothing recom-
mendation task. Some websites (such as www.
dresscodeguide.com) can provide recommen-
dations for the most suitable clothing for a spe-
cific occasion. Many current recommendation
systems consider the scenario during the rec-
ommendation process: given the hair color, eye
color, and skin color of the input body, plus a
wardrobe of clothing items, the outfit synthesis
system suggests a set of outfits subject to a par-
ticular dress code.
The Magic Closet is one such system.8 It
mainly targets at two clothing recommendation
scenarios. The first scenario is clothing suggestion.
As the top panel in Figure 3 shows, a user speci-
fies an occasion and the most suitable suits or
two separate clothing items (such as one t-shirt
and one pair of trousers) are suggested from the
user’s photo album. For this function, the Magic
Closet can be implemented as a mobile applica-
tion. The second scenario is clothing pairing. The
bottom panel in Figure 3 shows that a user
inputs an occasion and one reference clothing
item (such as a t-shirt the user wants to pair
with), and the most matched clothing from the
online shopping website is returned (such as a
skirt). The returned clothing should aestheti-
cally pair with the reference clothing and be
suitable for the specified occasion. For this func-
tion, the Magic Closet system can serve as a
plug-in application in any online shopping web-
site for shopping recommendations.
Two key principles were considered when
designing the Magic Closet. One is wearing prop-
erly, which means putting on decent, suitable
clothing. Such recommendations must con-
form to applicable dress codes, which are writ-
ten and unwritten rules with regard to clothing
and common sense. The other is wearing aes-
thetically, which are atheistic rules that need to
be followed when someone pairs upper and
lower body clothing items. For example, it may
not be appropriate to wear a red coat and a pair
of green pants together.
In the model learning process, to narrow the
semantic gap between the low-level visual fea-
tures of clothing and the high-level occasion
categories information, middle-level clothing
attributes are utilized. Attributes have proven
Within-scenariospare representation
αParts alignmentQuery dailyphoto
020 40 60 80 1000
0.5
1.0
1.5
2.0
Y X EZ
+≅ ……
Offline: Cross-scenario transfer learning
Online: Parts alignment and two-step feature transfer
Cross-scenariospare representation Zα Retrieved clothing
Reconstructionbasis
y
Figure 2. Framework of the Street-to-Shop clothing retrieval system. The proposed solution includes two key components: human/
clothing parts alignment to handle human pose variations and bridging cross-scenario discrepancies with an auxiliary daily photo
dataset. (This illustration originally appeared in previous work.7)
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useful in many computer vision tasks. The
Magic Closet utilizes seven defined multivalue
clothing attributes, including the category
attribute (such as jeans or skirt) and detail attrib-
utes, which describe certain properties of the
clothing (such as the color or pattern). The
clothing recommendation model is learned
through a unified latent support vector machine
(SVM) framework. The model integrates four
potential terms:
� visual features versus attribute,
� visual features versus occasion,
� attributes versus occasion, and
� attribute versus attribute.
Here the first three concern clothing-occa-
sion matching and the last one describes the
clothing-clothing matching. Embedding these
matching rules into the latent SVM model
explicitly ensures that the recommended cloth-
ing satisfies the wearing properly and wearing
aesthetically requirements simultaneously.
Future Directions of Clothing AnalysisThe key techniques for clothing analysis—
human/object detection, human pose estima-
tion, and image semantic segmentation—can
also be directly applied to the analysis of other
fashion items. Thus, it is likely that more works
will be conducted on the analysis of other fash-
ion items, such as bags, shoes, and earrings,
because there is a huge market for these fashion
items as well. However, these items are rela-
tively small compared with clothes, so tailored
features are demanded for item representation.
Another likely area of future research is vir-
tual fitting, which mainly involves techniques
for clothing analysis including retrieval and rec-
ommendation applications. In real life, custom-
ers expect to try on recommended clothing.
Thus, a virtual fitting would be a way to see the
virtual effects. In 2013, some large fashion
brands, including Adidas and Hugo Boss, imple-
mented online virtual fitting rooms in a bid to
reduce return rates and improve customer satis-
faction. However, the virtual fitting problem is
still far from solved. For example, it is challeng-
ing to virtually change the texture and pattern
of a t-shirt while maintaining correct clothes
deformation and shading. Another topic worth
studying is how to use an image-based approach
with a multicamera setup to transfer clothes.
Lastly, online clothing shopping is becom-
ing increasingly popular. In many online shop-
ping websites, such as Amazon.com, eBay.com,
and shopstyle.com, customers can conven-
iently find their favorite clothing by typing
I am at a conference, pleaserecommend something from my
photo album?
I am going on a date,please find something tomatch with this t-shirt?
(a) Attribute vs. occasion model
(b) Attribute vs. attribute model
Occasion-oriented clothing recommendation results
(c) Clothing attribute estimation
NoSleeve
wedding
sleeveless
straplessdress
silk
cotton
long_sleeve
jewelneckline
0.02
42
0.0759
0.0238
0.0161
0.01290.0182
Long_sleeve
Short_sleeve 0.41
0.68
0.29 0.29
Whitesleevelessstrapless
0.13
0.29
0.19
0.350.36
longnorm
al
short
Clothing recommendation model
Occasion-oriented clothing pairing results
(a) Suit (b) Upper + lower
++
Online shopping websites
T-shirt
Figure 3. Two typical clothing recommendation scenarios for the Magic Closet. (Top panel) Given a specified occasion, the system
suggests the most suitable clothing combinations from a photo album. The suggested clothing can be one or two pieces of clothing.
(Bottom panel) For clothing pairing, given an occasion and a reference piece of clothing, the clothing most suitable for the occasion
and most matched with the reference clothing is recommended from online shopping websites. (This illustration originally appeared
in previous work.8)
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keywords, such as “black, sleeveless cocktail
dress.” Also, some online websites, such as
www.dresscodeguide.com, www.chictopia.com,
www.mogujie.com, and www.meilishuo.com,
attempt to teach girls how to dress well. How-
ever, many challenges still exist when deploy-
ing current academic demo systems into real
application scenarios. In the coming years, we
expect to see more interaction between aca-
demia and industry, and practical and deploy-
able systems will be the common interest.
Makeover AnalysisResearch on makeover analysis typically in-
volves studies looking at facial and hair beauty.
Facial beauty studies include facial attractive-
ness prediction, facial geometry beautification,
and facial makeup synthesis, whereas hair
beauty research consists of hair segmentation
and modeling. After surveying the recent stud-
ies on facial and hair beauty as well as makeover
recommendation and synthesis systems, we
discuss future directions in makeover analysis.
Facial Beauty Study
Facial attractiveness largely depends on a per-
son’s precisely controlled symmetry, height of
the internal features, relative luminance of
different facial features, and the quality of the
skin. There are several major theories about
facial attractiveness, such as the composite
faces theory, the symmetry theory, the skin and
texture theory, the (geometric) facial feature
theory, the golden ratio theory, the facial thirds
theory, the facial fifths theory, the juvenilized
face theory, and the frontal versus lateral view
theory. For example, the composite faces theory
suggests that an average face is more attractive
than a normal one, but that a very beautiful
face may not be close to the average face.
Facial Attractiveness Prediction. There have
already been numerous works evaluating facial
attractiveness. For example, Douglas Gray and
his colleagues built a system to learn female
facial beauty and produce human-like predic-
tors.9 Instead of an absolute rating, they used a
pairwise rating to accelerate the rating process.
Active learning is introduced into the system to
avoid heavy manual labeling. A hierarchical
feed-forward model is learned directly from the
raw pixels to train a human-like predictor.
Tam Nguyen and his colleagues presented a
comprehensive study on how multiple modal-
ities (face, dress, and voice) jointly affect the
sense of female attractiveness (see Figure 4).10
k-wise preferencesfrom participants
Face Dressing Voice
Attributes annotation byworkers in mechanical Turk
VERY ATTRACTIVEATTRACTIVEQUITE ATTRACTIVE
Attractiveness score
Eye open
LipstickSmilingAND/OR
AND/ORSleevelessLong dress Skirt shape Pant shape
SmoothnessSinging
AttributesM2B dataset
HairstyleEye wear
Figure 4. Comprehensive
study of sensing human
beauty via
multimodality cues:
face, dress, and voice.11
The attractiveness
scores are given by
k-wise preferences from
participants. The
attribute model and the
attractiveness model
are jointly learned in a
dual-supervised model.
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They extended the pairwise rating to a more
user-friendly k-wise rating for large-scale attrac-
tiveness annotation. They further integrated
the attribute model and the attractiveness
model into a dual-supervised model to jointly
learn their relations. The commonalities and
differences of the Eastern and Western sensing
of attractiveness were also analyzed.
Facial Geometry Beautification. Facial geom-
etry beautification mainly aims to enhance the
attractiveness of facial shapes and components.
This kind of technology is helpful for plastic
surgery. Before a plastic surgery operation is per-
formed on patients, it is important for them to
fully understand the change in the facial
appearance after the operation. Digital facial
beautification makes it possible for a patient to
view the outcome during an early stage.
Tommer Leyvand and his colleagues pro-
posed a data-driven approach to optimize facial
shapes, while maintaining a close similarity
between the beautified and original faces.11
They trained a beautification engine on a data-
base of 92 Caucasian male and female faces and
rated the attractiveness score of each face.
Given a new face, 84 facial landmark points are
first detected, and a set of distances between
those landmarks are calculated to define a high-
dimensional face geometry space. Then the
nearby face with higher attractiveness rating is
searched by a K-nearest neighbor or SVM in the
face space to serve as a target of beautification.
Beautification is applied by calculating a 2D
warp field mapping from the original face to
the beautified face according to the correspond-
ing landmark locations.
Facial Makeup Synthesis. As opposed to facial
geometry beautification, digital facial makeup
synthesis mainly focuses on changing the facial
appearance. As a result of the huge market
potential, a variety of software applications have
been built for online cosmetic shops to help
women simulate the effects of makeup, such as
Taaz (www.taaz.com). Some image processing
software, such as Photoshop, can also be used to
synthesize the effects of makeup, but most of
them require heavy manual interaction work.
Most research works try to utilize before-
and-after image pairs created by professional
makeup artists to extract makeup. Their cos-
metic-transfer procedure defines makeup as
ratios of appearance change on before-and-after
image pairs.
Unlike these works, the approach proposed
by Dong Guo and Terence Sim only requires
after-makeup photos.12 This is more conven-
ient for real applications because the before-
and-after pairs are often difficult to obtain in
practice. Guo and Sim decomposed the test and
example images into face structure, skin detail,
and color layers, and they assumed that makeup
mainly exists in the skin detail and color layers.
Then the test face and the example face are
aligned by a thin plate spline (TPS) referring to
the 83 detected facial landmark points. Makeup
is transferred from the example image to the
test image, layer by layer, before being compos-
ited to generate the final effect.
Hair Beauty Study
A person’s overall attractiveness depends on not
only the internal face traits but on the external
facial regions, such as hair, as well. Still in its
early stage, the research of hair beauty mainly
focuses on hair segmentation and modeling.
Hair Segmentation. Hair segmentation is a
task that may benefit other problems, such as
hair style retrieval, virtual makeovers, and hair
sketching. The difficulty in modeling shape var-
iations makes it challenging. Nan Wang and his
colleagues proposed a part-based model to iden-
tify local parts.13 They proposed a measurable
statistic, called subspace clustering dependency
(SC-Dependency), to estimate the co-occur-
rence probabilities between local shapes. Then,
a Markov random field is utilized to formulate
this part identification problem and optimize
the effectiveness of the potential functions.
Hair Modeling. Hong Chen and Song-Chun
Zhu presented a generative sketch model for
hair analysis and synthesis.14 They proposed a
2D view-based method that treats hair images as
2D piecewise smooth vector (flow) fields. The
generative model is divided into three levels: the
high-frequency band of the hair image; the mid-
dle level of piecewise smooth vector field for the
hair orientation, gradient strength, and growth
directions; and the top level of attribute sketch
graph for representing the discontinuities in the
vector field. This model can then synthesize real-
istic hair images and stylistic drawings from a
sketch graph and Gaussian basis.
Makeover Recommendation and Synthesis
The Beauty e-Experts system15 is the first make-
over recommendation and synthesis system
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that helps users to select hairstyles and makeup
automatically and produces the synthesized
visual effects (see Figure 5). The main challenge
is determining how to model the complex rela-
tionships among different beauty and beauty-
related attributes for reliable recommendation
and natural synthesis.
To address this challenge, a large Beauty
e-Experts dataset was constructed that contains
1,505 images of beautiful female figures se-
lected from professional fashion websites. The
beauty attributes are labeled for each image
in the whole dataset. These beauty attributes,
including different hairstyles and makeup
types, are all adjustable. Their specific combina-
tion is considered the recommendation objec-
tive of the Beauty e-Experts system. To narrow
the gap between the high-level beauty attrib-
utes and low-level image features, a set of mid-
level beauty-related attributes, such as facial
traits and clothing properties, are also anno-
tated for the dataset.
Based on all these attributes, a multiple tree-
structured super-graph model is learned to
explore the complex relationships among these
attributes. As a generalization of a graph, a
super-graph can theoretically characterize any
type of relationships among different attributes
and thus provide a powerful recommendation.
A multiple tree-structured approximation is
proposed to reserve the most important
relationships and make the inference procedure
tractable. Based on the recommended results,
an effective and efficient facial image synthesis
module is designed to seamlessly synthesize the
recommended results into a facial image for the
user.
Future Directions of Makeover AnalysisOne area of possible future research is personal-
ized makeover recommendation. Different users
may have different makeover preferences. In
addition to personal tastes, different occasions
require different makeover effects. For example,
a woman attending school classes will likely
require different makeup than when she attends
a banquet. These differences also vary largely for
different groups. Asian women, for example,
may not like large eye shadow shapes because
their face shapes are more flat than Europeans.
Preferences may also vary with age. For exam-
ple, many seniors may not like colorful and
heavy makeup, so light makeup and face treat-
ment is enough for them. Thus, personal prefer-
ence, race, occasion, and age should all be
considered in makeover recommendations.
To satisfy such personal preferences, one pos-
sible solution is to consider using history infor-
mation about a person’s makeover preference.
People may share some of their own makeover
photos in Facebook and other photo-sharing
websites, and an improved recommendation
I have secretbeauty “experts”!
Beautye-experts
Long curls side part
Lip gloss
Eye shadow color
Eye shadow template
Foundation
Synthesize and share
Wow! You are sobeautiful today!
Figure 5. Beauty
e-Experts system.15
Based on the user’s
facial and clothing
characteristics, the
Beauty e-Experts system
automatically
recommends suitable
hairstyle and makeup
products to the user and
then produces the
synthesized visual
effects.
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model could be learned from such photos. In
addition, race-, occasion-, and age-oriented
makeover recommendations can also be mod-
eled by integrating with race, occasion, and age
information in the recommendation model.
Overall, the personalized makeover analysis is
particularly important for real applications, and
new models and learning methods need be
developed for this purpose.
Current research on makeover analysis is
mainly based on relatively small-scale datasets.
However, given the billions of photos available
on the Web, a scalable recommendation model
must be built. Thus, more efforts in benchmark
database construction are necessary, and more-
over, scalable models and learning methods
also need to be developed to satisfy the speed
requirements of real applications.
Lastly, makeover preferences are dynamic,
and new makeover-related photos are easily
available from the Web. An interesting direc-
tion may involve automatically mining make-
over trends from online content to intelligently
and dynamically serve users.
ConclusionThe promising future directions discussed in
both clothing and makeover analysis will need
to bridge the gap between academic research
and real industry demand. Given the huge
profit potential in the ever-growing consumer
fashion industry, the representative intelligent
fashion analysis techniques surveyed here are
just the beginning of this expanding research
field. MM
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Si Liu is a research fellow in the Learning and Vision
Research Group, Department of Electrical and Com-
puter Engineering, at National University of Singa-
pore. Contact her at [email protected].
Luoqi Liu is a doctoral student in the Learning and
Vision Research Group, Department of Electrical and
Computer Engineering, at National University of
Singapore. Contact him at [email protected].
Shuicheng Yan is an associate professor in the
Department of Electrical and Computer Engineering
at National University of Singapore and the found-
ing lead of the Learning and Vision Research Group.
Contact him at [email protected].
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