113 research outputs found
An investigation on local wrinkle-based extractor of age estimation
Research related to age estimation using face images has become increasingly important due to its potential use in various applications such as age group estimation in advertising and age estimation in access control. In contrast to other facial variations, age variation has several unique characteristics which make it a challenging task. As we age, the most pronounced facial changes are the appearance of wrinkles (skin creases), which is the focus of ageing research in cosmetic and nutrition studies. This paper investigates an algorithm for wrinkle detection and the use of wrinkle data as an age predictor. A novel method in detecting and classifying facial age groups based on a local wrinkle-based extractor (LOWEX) is introduced. First, each face image is divided into several convex regions representing wrinkle distribution areas. Secondly, these areas are analysed using a Canny filter and then concatenated into an enhanced feature vector. Finally, the face is classified into an age group using a supervised learning algorithm. The experimental results show that the accuracy of the proposed method is 80% when using FG-NET dataset. This investigation shows that local wrinkle-based features have great potential in age estimation. We conclude that wrinkles can produce a prominent ageing descriptor and identify some future research challenges. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved
Wrinkle Detection Using Hessian Line Tracking
Wrinkles play an important role in face-based
analysis. They have been widely used in applications such as
facial retouching, facial expression recognition and face age
estimation. Although a few techniques for wrinkle analysis have
been explored in the literature, poor detection limits the accuracy
and reliability of wrinkle segmentation. Therefore, an automated
wrinkle detection method is crucial to maintain consistency and
reduce human error. In this paper, we propose Hessian Line
Tracking (HLT) to overcome the detection problem. HLT is
composed of Hessian seeding and directional line tracking. It is
an extension of a Hessian filter; however it significantly increases
the accuracy of wrinkle localization when compared with existing
methods. In the experimental phase, three coders were instructed
to annotate wrinkles manually. To assess the manual annotation,
both intra- and inter-reliability were measured, with an accuracy
of 94% or above. Experimental results show that the proposed
method is capable of tracking hidden pixels; thus it increases
connectivity of detection between wrinkles, allowing some fine
wrinkles to be detected. In comparison to the state-of-the-art
methods such as the CUla Method (CUM), FRangi Filter (FRF),
and Hybrid Hessian Filter (HHF), the proposed HLT yields better
results, with an accuracy of 84%. This work demonstrates that
HLT is a remarkably strong detector of forehead wrinkles in 2D
images
Computational Intelligence in Automatic Face Age Estimation: A Survey
With the rapid growth of computational intelligence techniques, automatic face age estimation has achieved good accuracy that benefited real-world applications such as access control and monitoring, soft biometrics, and information retrieval. Over the past decade, many new algorithms were developed and previous surveys on face age estimation were either outdated or incomplete. Considering the importance of the expanding research in this topic, we aim to provide an up-to-date survey on the face age estimation techniques. First, we summarize the state-of-the-art databases and the performance metrics for face age estimation. Then, we review the age estimation techniques based on three categories of face features (local, global, and hybrid) and discuss different types of age learning algorithms. Finally, we identify the challenges and provide new insights for future research directions of fully automated face age estimation
Comparison of filtering methods for extracting transient facial wrinkle features
Facial local features comprise an essential information to identify a personal characteristic such as age, gender, identity and expression. One of the facial local features is a wrinkle. Wrinkle is a small furrow or crease in the skin. Recently, wrinkle detection has become a topic of interest in computer vision, where many researchers developed applications like age estimation, face detection, expression recognition, facial digital beauty and etc. However, most of the research focused on permanent wrinkles instead of transient wrinkles. Transient wrinkle can be seen during the movement of facial muscle such as a facial expression. This paper presents a comparison of filtering method for extracting transient wrinkles features. The filters that have been selected are Gabor wavelet and Kirsch operator. The extracted features are the number of wrinkles, the maximum perimeter of wrinkle, the average perimeter of wrinkle, total perimeter of wrinkle, the maximum area of the wrinkle, and the total area of the wrinkle. A total of 60 sets of data extracted from Cohn-Kanade database, images from internet and self-images. These images contain weak and strong transient wrinkles at forehead region. Features selection and analysis has been done to select which feature extraction method produces better wrinkle features that can be used for the classification of wrinkle detection system. The results show that both Gabor and Kirsch methods are successful to extract transient wrinkle features, where both methods scored 100% accuracy in the classification with SVM. However, Gabor method is slightly better than Kirsch method in term of detecting weak wrinkles. The Kirsch method requires an additional noise filtering method to eliminate noise particles after the convolution of Kirsch’s kernel. In conclusion, Gabor method is more applicable to a variety of applications than Kirsch method
Conditional Image Synthesis by Generative Adversarial Modeling
Recent years, image synthesis has attracted more interests. This work explores the recovery of details (low-level information) from high-level features. The generative adversarial nets (GAN) has led to the explosion of image synthesis. Moving away from those application-oriented alternatives, this work investigates its intrinsic drawbacks and derives corresponding improvements in a theoretical manner.Based on GAN, this work further investigates the conditional image synthesis by incorporating an autoencoder (AE) to GAN. The GAN+AE structure has been demonstrated to be an effective framework for image manipulation. This work emphasizes the effectiveness of GAN+AE structure by proposing the conditional adversarial autoencoder (CAAE) for human facial age progression and regression. Instead of editing on the image level, i.e., explicitly changing the shape of face, adding wrinkle, etc., this work edits the high-level features which implicitly guide the recovery of images towards expected appearance.While GAN+AE being prevalent in image manipulation, its drawbacks lack exploration. For example, GAN+AE requires a weight to balance the effects of GAN and AE. An inappropriate weight would generate unstable results. This work provides an insight to such instability, which is due to the interaction between GAN and AE. Therefore, this work proposes the decoupled learning (GAN//AE) to avoid the interaction between them and achieve a robust and effective framework for image synthesis. Most existing works used GAN+AE structure could be easily adapted to the proposed GAN//AE structure to boost their robustness. Experimental results demonstrate the correctness and effectiveness of the provided derivation and proposed methods, respectively.In addition, this work extends the conditional image synthesis to the traditional area of image super-resolution, which recovers the high-resolution image according the low-resolution counterpart. Diverting from such traditional routine, this work explores a new research direction | reference-conditioned super-resolution, in which a reference image containing desired high-resolution texture details is used besides the low-resolution image. We focus on transferring the high-resolution texture from reference images to the super-resolution process without the constraint of content similarity between reference and target images, which is a key difference from previous example-based methods
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
Towards Automation and Human Assessment of Objective Skin Quantification
The goal of this study is to provide an objective criterion for computerised
skin quality assessment. Humans have been impacted by a variety of face
features. Utilising eye-tracking technology assists to get a better understanding
of human visual behaviour, this research examined the influence of face
characteristics on the quantification of skin evaluation and age estimation.
The results revealed that when facial features are apparent, individuals do
well in age estimation. Also, this research attempts to examine the performance
and perception of machine learning algorithms for various skin attributes.
Comparison of the traditional machine learning technique to deep
learning approaches. Support Vector Machine (SVM) and Convolutional Neural
Networks (CNNs) were used to evaluate classification algorithms, with
CNNs outperforming SVM. The primary difficulty in training deep learning
algorithms is the need of large-scale dataset. This thesis proposed two
high-resolution face datasets to address the requirement of face images for
research community to study face and skin quality. Additionally, the study
of machine-generated skin patches using Generative Adversarial Networks
(GANs) is conducted. Dermatologists confirmed the machine-generated images
by evaluating the fake and real images. Only 38% accurately predicted
the real from fake correctly. Lastly, the performance of human perception and
machine algorithm is compared using the heat-map from the eye-tracking experiment
and the machine learning prediction on age estimation. The finding
indicates that both humans and machines predict in a similar manner
Gender and Age Classification of Human Faces for Automatic Detection of Anomalous Human Behaviour
In this paper, we introduce an approach to classify gender and age from images of human faces which is an essential part of our method for autonomous detection of anomalous human behaviour. Human behaviour is often uncertain, and sometimes it is affected by emotion or environment. Automatic detection can help to recognise human behaviour which later can assist in investigating suspicious events. Central to our proposed approach is the recently introduced transfer learning. It was used on the basis of deep learning and successfully applied to image classification area. This paper is a continuous study from previous research on heterogeneous data in which we use images as supporting evidence. We present a method for image classification based on a pre-trained deep model for feature extraction and representation followed by a Support Vector Machine classifier. Because very few data sets with labels of gender and age exist of face images, we build one dataset named GAFace and applied our proposed method to this dataset achieving excellent results and robustness (gender classification: 90.33% and age classification: 80.17% accuracy) approaching human performance
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