Visible Light Facial Recognition Facts & Figures
Visible Light
Facial Recognition has become the most spoken word in the current Biometric Industry.
The recent advancements in deep learning led to phenomenal developments in Computer
Vision Technology, especially in the area of facial recognition. With respect to
facial recognition performance based on deep network, there are two significant
factors: Structure of neural networks and quality of the data. Whereas, in
real-time applications, illumination change, which is nothing but changes in the
light intensity, is the most significant factor impacting the performance of
facial recognition algorithms.
Biometrics
and Facial Recognition
Biometrics
is an essential scientific filed of biological pattern recognition. In that, facial
recognition is one of the most attractive applications of biometric technology.
Nonetheless, facial recognition in real applications still pose greater challenges due
to the fact that face often undergo changes owing to various facial expression,
different appearances and angles as well as environmental factors such as different
light intensity.
However,
with recent advancements in biometric technology together with deep learning
and robust facial recognition algorithms, today it is made possible to verify
individuals’ face regardless of the attire, facial expressions, accessories and
environmental factors including light intensity and shadow.
Types
of Facial Recognition
There
are various facial recognition methods available, let’s take a closer look into
the technical details for a deeper understanding.
Traditional
Method
This
method identifies unique facial features such as relative position, size and
shape of individual’s facial attributes from an image and compares with other
existing data to recognize a person. On the other hand, certain algorithms
normalize a cluster of images taken from an individual and compress it into a reference
image data which is then used for facial recognition.Since, this method
extracts data from photographic image rather than the original face, it is
susceptible to print and video attacks.
Template
Based Method
This
method takes local facial features and their geometric relationship into
consideration and constructs a facial template using certain statistical tools
such as Support Vector Machines, Linear Discriminant Analysis, Kernel methods, Principal
Component Analysis, etc.
Piecemeal Method of Analysis
This is considered to be one of the famous methods of facial analysis using the whole face as a model with most relevant facial characteristics such as eyes or a combination various facial attribute. This category also contains Hidden Markov Model and Feature Processing.
3D
Facial Recognition
This
type of facial recognition employs 3D sensors to gather accurate details of
facial attributes such as shape, size, position and contour of face, eyes,
nose, chin, etc. from actual individuals by projecting structured light spectrum
onto the face in three-dimensional angles. In addition, this method is not
affected by light intensity and offers wide pose angle tolerance enhancing the
precision of facial recognition. However, this technique is slightly sensitive
to facial expressions as the data is already stored in pre-exiting
three-dimensional data points.
Skin Texture Analysis
This
method extracts the visible details of the facial skin such as lines,
patterns,spots, skin texture and pores of individuals into unique mathematical
representation, this mathematical representation is then used for facial
identification. Furthermore, this technique can also distinguish between
identical pairs enhancing the precision of facial recognition.
Neural Networks & Deep Learning
Neural
Networks-Based facial recognition is one of the most adopted methods in
biometric technology. It also the most sophisticated procedure of multi-dimensional
vector-based data analysis, which has taken the mathematical representation of facial
attributes to a higher magnitude. In this method neural networks are used to
recognise and align normalised faces by combining various statistical tools to
form a hybrid methodology of analysis for a multi-layer perception. This multilayer
perceptron is then used as a facial recognition system in association with deep
learning technology to automatically adapt to the changes in individuals’
facial attributes or external environment.
Multiplex Facial Recognition
As
every method has pros and cons, multiplex facial recognition utilizes all
possible combinations of robust facial recognition methods such as
Template-Based, 3D Recognition, Skin Textual Analysis, Neural Networks & Deep
Learning, etc to form a powerful facial recognition algorithm offering
outstanding performance with respect to speed and precision.
The
facial recognition systems employing this kind of approach will show multiple
attributes of various algorithms such as,
High
Speed Recognition
Wide
Pose Angle Tolerance
Precise
Recognition at Flexible Distance
Less
Sensitive to Facial Expressions
User-Identification
with Facial Accessories
Powerful
Anti-Spoofing Mechanisms
And
so on.
ZKTeco is one of the few companies in the word, which offers biometric systems with combination of multiple facial recognition algorithms such as ProFace X Series, SpeedFace-V5L Series, etc featuring Smart Visible Light Facial Recognition technology with advanced biometric algorithms for both face and palm verification.
The
ZK Visible Light Facial Recognition technology develops a 3D facial template
model via deep learning technology and provides wide pose angle tolerance in
all yaw, pitch and roll axis, which eliminates the need for maintaining a particular
pose or angle offering a free range of motion and automatically captures faces
of users.
Still
having confusions about Visible Light Facial Recognition technology? please
don’t hesitate to contact our Technical
Support Executives for further
information.