FOREIGN:
ADAFACE: QUALITY ADAPTIVE MARGIN FOR FACE
RECOGNITION
Authors:
Minchul Kim, Anil K. Jain, Xiaoming Liu Department of Computer Science and Engineering,
Michigan State University, East Lansing, MI, 48824
Published in CVPR2022 (Oral)
https://openaccess.thecvf.com/content/CVPR2022/html/Kim_AdaFace_Quality_Adaptive_Margi
n_for_Face_Recognition_CVPR_2022_paper.html
Recognition in low quality
face datasets is challenging
because facial attributes are
obscured and degraded.
Advances in margin-based
loss functions have resulted
in enhanced discriminability
of faces in the embedding
space. Further, previous
studies have studied the effect
of adaptive losses to assign
more importance to
misclassified (hard)
examples. In this work, we
introduce another aspect of
adaptiveness in the loss
function, namely the image
quality. We argue that the
strategy to emphasize
misclassified samples should
be adjusted according to their
image quality. Specifically,
the relative importance of
easy or hard samples should
be based on the sample's
image quality. We propose a
new loss function that
emphasizes samples of
The study explores AdaFace's
technical details, going over
how it was implemented and
how well it performed in
comparison to other face
recognition techniques. The
study probably sheds light on
the difficulties in face
recognition and how AdaFace
overcomes them to raise the
standard of face recognition
software as a whole.
The study that is being
presented presents AdaFace, a
novel method for tackling the
problem of face identification
in datasets with poor quality.
The suggested approach
makes use of an adaptive loss
function that modifies the
emphasis on simple or
complex instances based on
the image quality of face
samples. On several datasets,
the work shows enhanced
performance above the stateof-the-art techniques. These
include investigating more
reliable measures of image
quality, determining whether
or not they are generalizable
across various datasets,
analyzing the effects of image
preprocessing, assessing
computational effectiveness,
improving the interpretability
of the adaptive margin
function, researching
resilience to adversarial
attacks, and verifying
different difficulties based on
their image quality. Our
method achieves this in the
form of an adaptive margin
function by approximating
the image quality with feature
norms. Extensive experiments
show that our method,
AdaFace, improves the face
recognition performance over
the state-of-the-art (SoTA) on
four datasets (IJB-B, IJB-C,
IJB-S and TinyFace). Code
and models are released in
Supp.
effectiveness in practical
applications. Closely
examining these gaps may
lead to a more thorough
comprehension and
improvement of adaptive loss
functions for face recognition
under difficult circumstances.
FOREIGN:
Face Recognition: A Literature Review
Barnouti, N. H. (n.d.). Face Recognition: A Literature Review.
https://www.ijais.org/archives/volume11/number4/935-2016451597/
Authors: Nawaf Hazim Barnouti, Sinan Sameer Mahmood Al-dabbagh, Wael Esam Matti
Year of Publication: 2016
https://www.ijais.org/archives/volume11/number4/935-2016451597/
Face recognition have gained
a great deal of popularity
because of the wide range of
applications such as in
entertainment, smart cards,
information security, law
enforcement, and
surveillance. It is a relevant
subject in pattern recognition,
computer vision, and image
processing. Two major
methods are used for features
extraction, which can be
classified into appearancebased and Model-based
methods. Appearance-based
methods use global
representations to identify a
face. Model-based face
methods aim to construct a
model of the human face that
capture facial variations.
Image similarity is the
distance between the vectors
of two images. This paper
contains Four sections. The
first section discusses face
recognition applications with
examples. The second section
discuss the common feature
face recognition methods.
The third section discuss
distance measurement
classifiers. The fourth section
The researchers highlights the
use of facial recognition in
smart cards, entertainment,
information security, law
enforcement, and surveillance
while talking about the
technology's uses.
Appearance-based and
model-based feature
extraction techniques are the
two main approaches
identified. While modelbased approaches seek to
build a model incorporating
facial variability, appearancebased approaches use global
representations for face
identification.
Although facial recognition
techniques are introduced,
there is a lack of
comprehensive performance
evaluation and adaption
assessments for practical
situations in the literature. It
is unknown how resilient
model-based techniques are
to changes in facial features.
There is no talk about
potential biases in face
recognition databases, and
emerging technologies and
ethical issues are disregarded.
Filling such these gaps is
essential to gaining a more
thorough and useful
understanding of face
recognition technologies.
discuss different face
recognition databases.
FOREIGN:
Deep-Learning-Enhanced Multitarget Detection for End–Edge–
Cloud Surveillance in Smart IoT
Authors: X. Zhou, X. Xu, W. Liang, Z. Zeng and Z. Yan, "Deep-Learning-Enhanced Multitarget
Detection for End–Edge–Cloud Surveillance in Smart IoT," in IEEE Internet of Things Journal,
vol. 8, no. 16, pp. 12588-12596, 15 Aug.15, 2021, doi: 10.1109/JIOT.2021.3077449
Published in: IEEE Internet of Things Journal ( Volume: 8, Issue: 16, 15 August 2021)
https://ieeexplore.ieee.org/abstract/document/9422817/authors#authors
Along with the rapid
development of cloud
computing, IoT, and AI
technologies, cloud video
surveillance (CVS) has
become a hotly discussed
topic, especially when facing
the requirement of real-time
analysis in smart applications.
Object detection usually plays
an important role for
environment monitoring and
activity tracking in
surveillance system. The
emerging edge-cloud
computing paradigm provides
us an opportunity to deal with
the continuously generated
huge amount of surveillance
data in an on-site manner
across IoT systems. However,
the detection performance is
still far away from
satisfactions due to the
complex surveilling
environment. In this study,
we focus on the multitarget
detection for real-time
surveillance in smart IoT
systems. A newly designed
deep neural network model
Using the A-YONet model, a
hybrid of YOLO and
MTCNN, this study improves
multitarget identification in
smart IoT systems, which is
important for real-time
monitoring in smart
applications. By utilizing
edge-cloud computing for
minimal instruction, the
suggested approach enhances
accuracy in difficult
surveillance settings. Its
usefulness is confirmed by
experiments on real and
public datasets, highlighting
its significance in improving
real-time surveillance for
intelligent Internet of Things
applications.
There are several limitations
in our knowledge of AYONet's practical usability
and efficacy because the
study on its multitarget
detection in smart IoT
systems does not investigate
its scalability, adaptability,
robustness, ethical
implications, or comparative
performance comparison with
existing models. For practical
implementation in a variety
of monitoring contexts,
closing these gaps is
essential.
called A-YONet, which is
constructed by combining the
advantages of YOLO and
MTCNN, is proposed to be
deployed in an end-edgecloud surveillance system, in
order to realize the
lightweight training and
feature learning with limited
computing sources. An
intelligent detection
algorithm is then developed
based on a preadjusting
scheme of anchor box and a
multilevel feature fusion
mechanism. Experiments and
evaluations using two data
sets, including one public
data set and one homemade
data set obtained in a real
surveillance system,
demonstrate the effectiveness
of our proposed method in
enhancing training efficiency
and detection precision,
especially for multitarget
detection in smart IoT
application developments.
Vision-based Crowd Counting and Social Distancing Monitoring
using Tiny-YOLOv4 and DeepSORT
Author’s
J. C. Valencia, E. P. Dadios, A. M. Fillone, J. C. V. Puno, R. G. Baldovino and R. K. C.
Billones, "Vision-based Crowd Counting and Social Distancing Monitoring using Tiny-YOLOv4
and DeepSORT," 2021 IEEE International Smart Cities Conference (ISC2), Manchester, United
Kingdom, 2021, pp. 1-7, doi: 10.1109/ISC253183.2021.9562868.
https://ieeexplore.ieee.org/abstract/document/9562868
With the novel coronavirus,
social distancing and crowd
monitoring became vital in
managing the spread of the
virus. This paper presents a
desktop application that
utilizes Tiny-YOLOv4 and
DeepSORT tracking
algorithm to monitor crowd
count and social distancing in
a top-view camera
perspective. The application
is able to process video files
or live camera feed such as
CCTV or surveillance
cameras and generate reports
indicating people detected per
unit time, percentage of social
distancing per unit time,
detection and social
distancing logs as well as
color-coded bounding boxes
to indicate if the detected
people are following social
distancing protocols.
This work, which presents a
desktop program for crowd
surveillance and social
distance using Tiny-YOLOv4
and the DeepSORT tracking
algorithm, is extremely
pertinent in light of the
COVID-19 pandemic. After
processing video files or live
camera feeds, the application
generates detailed logs, social
distance percentages, and
reports on people it has
detected. Color-coded
bounding boxes are a useful
tool for crowd control in realtime during public health
emergencies because they
visually signal adherence to
social distancing standards.
There are some insufficient
studies in the article on a
desktop program for social
distancing and COVID-19
crowd surveillance. There are
no discussions on the
accuracy and dependability of
the Tiny-YOLOv4 and
DeepSORT tracking
algorithms, adaptation of the
algorithm to various
surveillance contexts, or
performance in real-world
scenarios. It is lacking the
implementation of privacypreserving measures and
addressing privacy concerns.
Furthermore, a vacuum in
knowledge regarding the
performance of the suggested
application remains due to the
lack of a comparative
analysis with current
solutions. Filling in these
gaps is essential to a thorough
assessment of the
applicability's accuracy,
practicality, and ethical
implications.
LOCAL:
The Technology Adoption and Governance of Artificial Intelligence
in the Philippines
Author’s
Ronnie S. Concepcion
Electronics and Communications Engineering Department, De La Salle University, Manila,
Philippines
Rhen Anjerome R. Bedruz
Manufacturing Engineering and Management Department, De La Salle University, Manila,
Philippines
Alvin B. Culaba
Mechanical Engineering Department, De La Salle University, Manila, Philippines
Elmer P. Dadios
Manufacturing Engineering and Management Department, De La Salle University, Manila,
Philippines
Athena Rosz Ann R. Pascua
Electronics and Communications Engineering Department, De La Salle University, Manila,
Philippines
https://ieeexplore.ieee.org/abstract/document/9072725
Artificial intelligence is
primed to disrupt our society
and the industry. The AI
trend of technological
singularity is continuously
accelerating and is being
employed to the different
facets of humanity from
education, medicine,
business, engineering, arts
and the like. Government and
private companies have been
hooked up with this fast
pacing technology. AI may
displace some non-digital
jobs that performs heavy load
and repetitive tasks, but it
certainly augments labor
shortage by realigning the
workforce competitiveness to
what the technology requires.
Artificial Intelligence is
rapidly changing education,
healthcare, business, and
other sectors of society and
economy. It addresses labor
shortages and increases
worker competitiveness, even
though it might eliminate
some employment. The
Philippines can take the lead
in the world in AI adoption
by putting the PDP 2017–
2022 and its HNRDA policies
into practice.
Although there are a number
of research gaps, the
summary highlights how AI
has the potential to
revolutionize the Philippines.
It doesn't go into detail on
possible job displacement,
doesn't look into adoption
barriers, and doesn't provide
particular instances of how
AI affects productivity.
Furthermore, it highlights the
significance of technological
governance without getting
into specific difficulties.
Additional investigation may
yield valuable perspectives on
the societal implications,
obstacles to adoption,
productivity measures,
governance issues, and
The diffusion of AI
technology is necessary for
mental shift of the
government and industry
leaders to adopt the
technology. Research and
development is very
promising to uplift mankind
to faster productivity and
positively affect the industries
in international perspective.
The Philippines is still coping
up with the adoption of AI
system, but it can steer up
globally by strengthening the
technology governance of
strictly implementing the
policies with measures the
PDP 2017-2022 and its
HNRDA.
factual data linking the use of
AI to worldwide leadership.
For a comprehensive grasp of
AI's consequences in the
Philippines and its place in
the world at large, these gaps
must be filled.
Design of face detection and recognition system for smart home
security application
Author’s:
Dwi Ana Ratna Wati
Universitas Islam Indonesia, Yogyakarta, Daerah Istimewa Yogyakart, ID
Dika Abadianto
Department of Electrical Engineering, Universitas Islam Indonesia, Yogyakarta, Indonesia
Published in: 2017 2nd International conferences on Information Technology, Information
Systems and Electrical Engineering (ICITISEE)
https://ieeexplore.ieee.org/abstract/document/8285524
This research designs face
detection and recognition
systems for smart home
security application. The
design is implemented using
MyRIO 1900 and
programmed using
LabVIEW. The connection
between myRIO and
computer is wifi network.
The image of a person is
acquired via webcam
connected to MyRIO using
USB cable. The face
detection system is built
based on the template
matching, while the face
recognition is based on the
principle component analysis.
The testing is done to
examine the performance of
the face detection in various
change of distance, light
intensity, light position
angles, person's accessories
and shirt colour. The face
detection modul has good
performance in some
conditions as distance
between the person and the
camera is less than 240 cm,
person doesn't use accessories
that cover part of face, person
doesn't use shirt with colour
similar to skin colour, and
background colour is
difference from skin colour.
While the face recognition
system has 80% of accuracy
when it is tested using
realtime image. The
combination with password is
needed in order to increase
Because it creates face
detection and recognition
algorithms specifically for
smart home security
applications, this research is
extremely pertinent. The
design connects via wifi and
uses LabVIEW and MyRIO
1900 to capture photographs
of people using a webcam.
Principal component analysis
is the foundation for face
recognition, whereas template
matching is used by the face
detection system. Testing
demonstrates good
performance within
predetermined boundaries
under a variety of situations.
In tests conducted in real
time, the facial recognition
system reaches an accuracy
of 80%. Since adding a
password improves security,
this research is useful and
applicable to actual smart
home security systems.
Although the system
performs well in certain
scenarios, there are no
specifics about possible
testing constraints in the
abstract. Further investigation
is required into the system's
scalability and adaptability to
various hardware and
software settings. The
robustness of the algorithm to
changes in facial expressions,
positions, and environmental
conditions is not covered in
the abstract, and there are no
details provided regarding the
dataset that was used to
assess the accuracy of face
recognition. Moreover,
further research is necessary
to identify any potential
weaknesses and concerns
with the suggested passwordprotected facial recognition
system. In real-world smart
home security systems,
closing these gaps is crucial
to improving the system's
security, scalability, and
dependability.
the security level as it is
applied in real smart home
security systems.