Hierarchical age estimation using enhanced facial features.
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Date
2018
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Abstract
Ageing is a stochastic, inevitable and uncontrollable process that constantly affect
shape, texture and general appearance of the human face. Humans can easily determine
onesโ gender, identity and ethnicity with highest accuracy as compared to
age. This makes development of automatic age estimation techniques that surpass
human performance an attractive yet challenging task. Automatic age estimation
requires extraction of robust and reliable age discriminative features. Local binary
patterns (LBP) sensitivity to noise makes it insufficiently reliable in capturing age
discriminative features. Although local ternary patterns (LTP) is insensitive to noise,
it uses a single static threshold for all images regardless of varied image conditions.
Local directional patterns (LDP) uses k directional responses to encode image gradient
and disregards not only central pixel in the local neighborhood but also 8 ๔ k
directional responses. Every pixel in an image carry subtle information. Discarding
8 ๔ k directional responses lead to lose of discriminative texture features. This
study proposes two variations of LDP operator for texture extraction. Significantorientation
response LDP (SOR-LDP) encodes image gradient by grouping eight
directional responses into four pairs. Each pair represents orientation of an edge
with respect to central reference pixel. Values in each pair are compared and the
bit corresponding to the maximum value in the pair is set to 1 while the other is
set to 0. The resultant binary code is converted to decimal and assigned to the central
pixel as itsโ SOR-LDP code. Texture features are contained in the histogram of
SOR-LDP encoded image. Local ternary directional patterns (LTDP) first gets the
difference between neighboring pixels and central pixel in 3 3 image region. These
differential values are convolved with Kirsch edge detectors to obtain directional
responses. These responses are normalized and used as probability of an edge occurring
towards a respective direction. An adaptive threshold is applied to derive
LTDP code. The LTDP code is split into its positive and negative LTDP codes. Histograms
of negative and positive LTDP encoded images are concatenated to obtain
texture feature. Regardless of there being evidence of spatial frequency processing
in primary visual cortex, biologically inspired features (BIF) that model visual cortex
uses only scale and orientation selectivity in feature extraction. Furthermore,
these BIF are extracted using holistic (global) pooling across scale and orientations
leading to lose of substantive information. This study proposes multi-frequency BIF
(MF-BIF) where frequency selectivity is introduced in BIF modelling. Local statistical
BIF (LS-BIF) uses local pooling within scale, orientation and frequency in n n
region for BIF extraction. Using Leave-one-person-out (LOPO) validation protocol,
this study investigated performance of proposed feature extractors in age estimation
in a hierarchical way by performing age-group classification using Multi-layer
Perceptron (MLP) followed by within age-group exact age regression using support
vector regression (SVR). Mean absolute error (MAE) and cumulative score (CS) were
used to evaluate performance of proposed face descriptors. Experimental results on
FG-NET ageing dataset show that SOR-LDP, LTDP, MF-BIF and LS-BIF outperform
state-of-the-art feature descriptors in age estimation. Experimental results show that
performing gender discrimination before age-group and age estimation further improves
age estimation accuracies. Shape, appearance, wrinkle and texture features
are simultaneously extracted by visual system in primates for the brain to process
and understand an image or a scene. However, age estimation systems in the literature
use a single feature for age estimation. A single feature is not sufficient enough
to capture subtle age discriminative traits due to stochastic and personalized nature
of ageing. This study propose fusion of different facial features to enhance their
discriminative power. Experimental results show that fusing shape, texture, wrinkle
and appearance result into robust age discriminative features that achieve lower
MAE compared to single feature performance.
Description
Doctor of Philosopy in Computer Science, University of KwaZulu-Natal, Westville, 2018.