1 research outputs found
Face age estimation using wrinkle patterns
Face age estimation is a challenging problem due to the variation of craniofacial growth,
skin texture, gender and race. With recent growth in face age estimation research, wrinkles
received attention from a number of research, as it is generally perceived as aging
feature and soft biometric for person identification. In a face image, wrinkle is a discontinuous
and arbitrary line pattern that varies in different face regions and subjects.
Existing wrinkle detection algorithms and wrinkle-based features are not robust for face
age estimation. They are either weakly represented or not validated against the ground
truth. The primary aim of this thesis is to develop a robust wrinkle detection method
and construct novel wrinkle-based methods for face age estimation. First, Hybrid Hessian
Filter (HHF) is proposed to segment the wrinkles using the directional gradient
and a ridge-valley Gaussian kernel. Second, Hessian Line Tracking (HLT) is proposed
for wrinkle detection by exploring the wrinkle connectivity of surrounding pixels using a
cross-sectional profile. Experimental results showed that HLT outperforms other wrinkle
detection algorithms with an accuracy of 84% and 79% on the datasets of FORERUS
and FORERET while HHF achieves 77% and 49%, respectively. Third, Multi-scale
Wrinkle Patterns (MWP) is proposed as a novel feature representation for face age
estimation using the wrinkle location, intensity and density. Fourth, Hybrid Aging Patterns
(HAP) is proposed as a hybrid pattern for face age estimation using Facial Appearance
Model (FAM) and MWP. Fifth, Multi-layer Age Regression (MAR) is proposed as
a hierarchical model in complementary of FAM and MWP for face age estimation. For
performance assessment of age estimation, four datasets namely FGNET, MORPH,
FERET and PAL with different age ranges and sample sizes are used as benchmarks.
Results showed that MAR achieves the lowest Mean Absolute Error (MAE) of 3.00
( 4.14) on FERET and HAP scores a comparable MAE of 3.02 ( 2.92) as state of the
art. In conclusion, wrinkles are important features and the uniqueness of this pattern
should be considered in developing a robust model for face age estimation