Albert M. (ed.) Biometrics - Unique and Diverse Applications in Nature, Science, and Technology
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Facial Expression Recognition
81
Classifiers
LDA QDC NNC
Recognition rate
81.89% 80.11% 78.53%
Table 3. Average recognition rate of faces from BU-3DFE database using the SSV-based
features.
Input/Output
Anger
(%)
Disgust
(%)
Fear
(%)
Happiness
(%)
Sadness
(%)
Surprise
(%)
Anger
82.64
3.48 4.17 3.47 4.86 1.39
Disgust
7.64
78.47
3.48 5.56 2.08 2.78
Fear
4.27 3.57
72.59
12.50 5.66 1.39
Happiness
2.78 5.56 8.33
83.33
0.00 0.00
Sadness
4.17 3.47 11.11 0.00
81.25
0.00
Surprise
0.00 0.00 4.17 2.78 0.00
93.06
Table 4. Confusion matrix of the LDA classifier for BU-3DFE database.
6. Evaluation of expression recognition using ADSIP database
The performance evaluation using the ADSIP database is an extension of that based on the
BU-3DFE database, which aims to check the stability of the proposed methodology with a
statistical shape model built from one database and tested using a different database.
Furthermore, it is also used to assess the performance of the proposed algorithm against
human observers.
As explained in Section 5.2, the statistical shape model was built based on 450 faces
randomly selected from the BU-3DFE database. One hundred static 3-D faces were
randomly chosen as the testing faces from the 210 dynamic 3-D facial sequences in the
ADSIP database, with each one occurring approximately at the maximum of the expression.
The selected test set contained 10 subjects with six different facial expressions at various
intensities. For each testing face represented by its SSV, the three classifiers LDA, QDC and
NNC, were used for the facial expression classification.
Table 5 shows the average results obtained from expression recognition using the three
classifiers. It can be seen that the LDA again achieved the highest recognition rate of 72.84%.
Although this result is worse than the result obtained using the test faces from the BU-3DFE
database, it is around 2.5% higher than the mean recognition rate achieved by the human
observers for those ‘extreme’ expressions in the same ADSIP database shown in Table 1, and
it is much better than the human observers’ mean recognition rates for the other two
intensity levels of expressions.
Classifiers
LDA QDC NNC
Recognition rate
72.84% 69.31% 70.54%
Table 5. Average recognition rate of the SSV-based feature from ADSIP database.
The confusion matrix of the LDA is shown in Table 6. It can be seen that the happiness is the
most recognisable expression and followed by the surprise expression. Fear and sadness are
the expressions which are often confused with others.
Biometrics - Unique and Diverse Applications in Nature, Science, and Technology
82
Input/Output
Anger
(%)
Disgust
(%)
Fear
(%)
Happiness
(%)
Sadness
(%)
Surprise
(%)
Anger
73.08
11.54 0.00 1.92 13.46 0.00
Disgust 11.54
71.15
9.62 1.92 1.92 3.85
Fear 1.92 11.54
55.77
17.31 13.46 0.00
Happiness 2.08 0.00 10.83
87.08
0.00 0.00
Sadness 22.92 0.00 10.42 0.00
66.67
0.00
Surprise 0.00 6.25 8.33 0.00 2.08
83.33
Table 6. Confusion matrix of the LDA classifier for ADSIP database.
7. Discussion and conclusions
This chapter has introduced concepts related to automatic facial expression recognition.
Although these have included description of general issues relevant to such problem, the
main emphasis has been on a review of the recent developments in the corresponding
processing pipeline including: data acquisition, face normalisation, feature extraction and
subsequent classification. The available facial expression databases were also introduced to
provide complete information about available options for algorithm validation. To make
these ideas clearer one of the algorithm, using shape space vector (SSV) calculated for 3D
static facial data, was described in detail. The algorithm consists of model building and
model fitting. In the model building stage, a statistical shape model (SSM) is constructed
from a set of training data. In the model fitting stage, the built SSM is aligned and matched
to the input faces through iterative estimation of pose and shape parameters, and such
estimated shape parameters (embedded in the SSV) are used as a feature vector for facial
expression recognition.
In order to examine the facial expression recognition performance offered by the SSV
representation method, two recently developed 3-D facial expression databases were used.
The evaluation results based on the BU-3DFE database show that the SSV based
representation method is capable of simulation and interpretation of 3-D human facial
expressions. The test preformed with the ADSIP database indicates that the method can be
used with data collected using different acquisition protocols. More significantly, the
recognition rates for different facial expressions obtained using the SSV based
representation are shown to be similar to those obtained using the human observers.
The temporal information plays a vital role in the facial expression recognition. An analysis
of a dynamic facial sequence instead of a single image is likely to improve the accuracy and
robustness of the facial expression recognition systems (Sun & Yin, 2008). To reflect this new
trend in facial data analysis the chapter includes short information about available 3D
dynamic datasets.
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0
Implications of Adult Facial Aging on Biometrics
Midori Albert, Amrutha Sethuram and Karl Ricanek
University of North Carolina Wilmington,
Institute for Interdisciplinary Studies in Identity Sciences
USA
1. Introduction
1.1 Statement of the problem
Single features of the human face, facial components, as well as the human face taken as a
whole may be viewed as a biometric tool for purposes of individual human identification.
Using computer technology, automated face recognition (FR) systems have been created to
match individual faces from print and digital photographs and video to faces of the same
individual whose image is stored among many others in a computer database. One of the
primary problems that arise with FR systems is how to contend with the passage of time and
the resultant effects of facial aging. Enrolled, i.e. gallery, images become difficult to match
against a query, i.e. probe, image as the time span between the gallery and the probe increases.
Several researchers have highlighted this temporal performance degradation (Pentland &
Choudhury, 2000; Phillips et al., 2000; Ricanek et al., 2006) .To meet this challenge, two
approaches have been taken: (1) research what is known about adult age-related craniofacial
morphological changes to better understand how to (2) synthetically age individuals from
a facial image, or rather, develop workable artificial age progression techniques to anticipate
how an individual’s face may appear years down the road. In addition to probing the question
of how a person’s face may age across the adult lifespan, other facial appearance questions
have arisen, such as how might a person’s face change simply over the course of one day?
Herein we present an overview of face aging, its implications on biometrics, specifically FR
systems, and provide an example of our work via relaying results of experiments designed
to measure facial age changes and artificially synthesize facial images, all in an effort to
improve FR systems and ensure that facial features, when used as a biometric tool, are reliable,
consistent, and accurate with replicable results.
1.2 Overview
As adults age, myriad changes occur diachronically within the craniofacial complex. Notable
soft tissue modifications may be seen across each decade of adult life that passes. As
well, subtle hard tissue or bony changes slightly alter the overall shape of the human face,
mainly in the dentoalveolar region. These age-related changes affect the accuracy and
efficacy of biometric techniques concerning face recognition, namely computer automated
face recognition (FR) systems. At the University of North Carolina Wilmington (UNCW), the
Institute for Interdisciplinary Studies in Identity Sciences (IISIS), is composed of a team of
researchers (formerly the Face Aging Group), dedicated to studying a wide variety of topics
5
related to face aging, facial age progression or synthetic face aging, and facial expression, with
the goal of improving computer automated face recognition (FR) technology. IISIS comprises
faculty and professional researchers, a post-doctoral scholar, graduate and undergraduate
students and high school interns working on multiple aspects of human identification using
facial features. In this chapter, we provide an overview of intrinsic and extrinsic factors
that affect human adult craniofacial aging, and the ways in which these changes have been
considered in research devoted to developing effective FR systems. We include an example of
our work; we present results of an experiment conducted to evaluate FR systems and discuss
synthetic aging techniques. We conclude with future goals.
2. Intrinsic and extrinsic factors causing facial aging
2.1 Intrinsic face aging factors
Intrinsic aging is caused by internal biological factors whereas extrinsic aging is caused by
environmental influences. Intrinsic factors causing facial aging are mainly due to the natural
changes that occur as soft tissues lose their elasticity, muscle tone, and volume (Coleman &
Grover, 2006; Isse, 2008) as well as the bony shape modifications resulting from the lifelong
and ongoing process of bone remodeling (Sadick et al., 2009; Zimbler et al., 2001). Affecting
these changes are an individual’s biological sex, ethnicity, and idiosyncratic features (i.e.,
features purely unique to the individual such as hyperdynamic facial expressions).
2.2 Extrinsic face aging factors
Extrinsic factors influencing facial aging may be due to lifestyle such as diet, drug use, and
or smoking (although this is debated, (O’Hare et al., 1999; Taister et al., 2000)) but the main
cause of skin aging is exposure to solar ultraviolet rays known as photoaging (Victor, 2003).
3. Body composition
Body composition is known to change as people age in adulthood. Muscle fibers atrophy
and fat is accumulated. Appendages become thinner while abdominal girth expands due to
a general weakening of abdominal muscles (Overfield, 1995). The ways in which muscle
loss and weight gain affect the aging face are not entirely understood as there is a paucity
of published literature specifically addressing weight gain and loss in and around the face.
However, it is often anecdotally noted that weight gain in the face typically results in a more
youthful appearance. This is due to facial folds and creases being buoyed and filled out by
facial fat. Indeed several websites contain commentary that after weight loss of around 30
pounds, rhytides and wrinkles increased (Peertrainer., 2009). Generally, fat stored in the face
shifts position as age advances. Fat from the upper cheeks diminishes and the face may take
on a more hollow appearance. Fat that droops toward the jawline creates the appearance of
jowls (Lipotherapy, 2009).
4. Ways in which the adult craniofacial complex changes with age
Adult age-related craniofacial changes comprise soft tissue and hard tissue changes.
Beginning as early as the 20’s, the soft tissues of the face age; however, the changes are most
noticeable between the 40’s and 50’s and continue to become more pronounced between the
50’s and 60’s (Albert et al., 2007; Taylor, 2001). Bony changes that occur in adolescence and
young adulthood are most likely a continuation of skeletal growth and development, while
changes due to remodeling occur from young adulthood on into senescence. Some typical
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