74 research outputs found
Comparative study of human age estimation based on hand-crafted and deep face features
In the past few years, human facial age estimation has drawn a lot of attention
in the computer vision and pattern recognition communities because of its important
applications in age-based image retrieval, security control and surveillance, biomet-
rics, human-computer interaction (HCI) and social robotics. In connection with these
investigations, estimating the age of a person from the numerical analysis of his/her
face image is a relatively new topic. Also, in problems such as Image Classification
the Deep Neural Networks have given the best results in some areas including age
estimation.
In this work we use three hand-crafted features as well as five deep features
that can be obtained from pre-trained deep convolutional neural networks. We do a
comparative study of the obtained age estimation results with these features
Analysis of facial expressions: experiments on multiple databases
This master thesis compares different face descriptors using classification techniques in
order to classify emotions in images of faces of people of different ethnicities and ages,
male and female. The comparison is done between hand-crafted features such as LBP and
HOG and more modern features such as some pre-trained neural networks. The proposed
methods were used on different databases, using different image sizes and cropping and
standardizing all the images. The experimental results showed that some of the hand-
crafted features were better that the pre-trained neural networks. To facilitate replication
of our experiments the MATLBAB source code will be available at https://github.
com/nagwlei/FaceEmotions
On the Influence of Ageing on Face Morph Attacks: Vulnerability and Detection
Face morphing attacks have raised critical concerns as they demonstrate a new
vulnerability of Face Recognition Systems (FRS), which are widely deployed in
border control applications. The face morphing process uses the images from
multiple data subjects and performs an image blending operation to generate a
morphed image of high quality. The generated morphed image exhibits similar
visual characteristics corresponding to the biometric characteristics of the
data subjects that contributed to the composite image and thus making it
difficult for both humans and FRS, to detect such attacks. In this paper, we
report a systematic investigation on the vulnerability of the
Commercial-Off-The-Shelf (COTS) FRS when morphed images under the influence of
ageing are presented. To this extent, we have introduced a new morphed face
dataset with ageing derived from the publicly available MORPH II face dataset,
which we refer to as MorphAge dataset. The dataset has two bins based on age
intervals, the first bin - MorphAge-I dataset has 1002 unique data subjects
with the age variation of 1 year to 2 years while the MorphAge-II dataset
consists of 516 data subjects whose age intervals are from 2 years to 5 years.
To effectively evaluate the vulnerability for morphing attacks, we also
introduce a new evaluation metric, namely the Fully Mated Morphed Presentation
Match Rate (FMMPMR), to quantify the vulnerability effectively in a realistic
scenario. Extensive experiments are carried out by using two different COTS FRS
(COTS I - Cognitec and COTS II - Neurotechnology) to quantify the vulnerability
with ageing. Further, we also evaluate five different Morph Attack Detection
(MAD) techniques to benchmark their detection performance with ageing.Comment: Accepted in IJCB 202
Neonatal pain detection in videos using the iCOPEvid dataset and an ensemble of descriptors extracted from Gaussian of Local Descriptors
Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges
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