AU2021103335A4 - System for productivity determination using machine learning based face recognition - Google Patents
System for productivity determination using machine learning based face recognition Download PDFInfo
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- AU2021103335A4 AU2021103335A4 AU2021103335A AU2021103335A AU2021103335A4 AU 2021103335 A4 AU2021103335 A4 AU 2021103335A4 AU 2021103335 A AU2021103335 A AU 2021103335A AU 2021103335 A AU2021103335 A AU 2021103335A AU 2021103335 A4 AU2021103335 A4 AU 2021103335A4
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- 238000010801 machine learning Methods 0.000 title claims abstract description 20
- 238000000034 method Methods 0.000 claims description 47
- 230000002996 emotional effect Effects 0.000 claims description 16
- 230000003213 activating effect Effects 0.000 claims description 15
- 238000004891 communication Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 4
- 230000008451 emotion Effects 0.000 abstract description 19
- 238000004458 analytical method Methods 0.000 abstract description 14
- 238000005516 engineering process Methods 0.000 abstract description 9
- 238000010420 art technique Methods 0.000 abstract 1
- 230000006399 behavior Effects 0.000 description 18
- 230000008921 facial expression Effects 0.000 description 18
- 230000014509 gene expression Effects 0.000 description 9
- 241000282414 Homo sapiens Species 0.000 description 8
- 230000008909 emotion recognition Effects 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 230000008450 motivation Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000036651 mood Effects 0.000 description 2
- 230000001537 neural effect Effects 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000001815 facial effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000003058 natural language processing Methods 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 230000011273 social behavior Effects 0.000 description 1
- 230000001755 vocal effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/174—Facial expression recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
Abstract
SYSTEM FOR PRODUCTIVITY
DETERMINATION USING MACHINE
LEARNING BASED FACE RECOGNITION
ABSTRACT
The present invention is related to a system for productivity
determination using machine learning based face recognition. The
objective of present invention is to solve the anomalies presented in the
prior art techniques related to employee; productivity analysis using
advance face recognition technologies.
26
DRAWINGS
FACE RECOGNITION UNIT
EMPLOYEE EMOTION DETERMINATION EMPLOYEE
DATA ff m LOCATION I
PROUCTIVITY
OUTCOME
FIGURE 1
29
Description
EMPLOYEE DATA ff I EMOTION DETERMINATION m EMPLOYEE LOCATION
FIGURE 1
[001]. The present invention relates to the technical field of biometric
and machine leaning.
[002]. The presents invention related to the technical field of emotion
[003]. More particularly, the present invention is related to a system for
productivity determination using machine learning based face
recognition.
[004]. Face recognition technology is currently a hot spot in biometrics
technology. It has important applications in security, attendance, and
robot applications. It is recognized as the least intrusive and most convenient biometric technology. In view of this, the purpose of the present invention is to provide a face recognition method and system, which can improve the face recognition rate under the condition that the illumination changes greatly and the face posture is random.
[005]. Facial emotion recognition (FER) is a major concern in the
fields of computer vision and artificial intelligence owing to its
significant academic and commercial potential.
[006]. Some of the prior work is listed herewith:
[007]. CN108764047A Group emotion behavior analysis method and
apparatus, electronic device, medium and product Embodiments of the
invention disclose a group emotion behavior analysis method and
apparatus, an electronic device, a medium and a product. The method
comprises the steps of carrying out image acquisition on a group
comprising at least one person to obtain at least one group image;
executing human face recognition on the at least one group image to
obtain at least one human face image, and/or executing human body
recognition on the at least one group image to obtain at least one human
body image.
[008]. CN109472212A An emotion analysis check-in interaction
system based on human face recognition The invention discloses an
emotion analysis check-in interaction system based on human face
recognition, the invention can achieve the purpose of check-in by the
way of face recognition or the combination of fingerprint and face
recognition, avoiding the problem of inaccurate check-in information
in common check-in mode, Moreover, since the check-in method of
face recognition can collect the image information of the sign-in
person, so that the emotion analysis module can analyze the mood of
the sign-in personnel on the day according to the image, and then
sends the motivator's discourse through the motivating interaction
module, Emotions, songs, or music to the check-in person's cell
phone, encourage and encourage the sign-in personnel, When it is
used in the company, it is helpful for the manager to analyze the
character of the employee, so as to arrange the working tasks more
reasonably. In addition, it is also helpful for the manager to grasp the
working state of the employee in real time.
[009]. CN107833018A Face recognition technology-based
enterprise management system The present invention discloses a
face recognition technology-based enterprise management system
which relates to the enterprise management technology field, and
comprises a work attendance management sub-system, an employee
motivation sub-system and a warehouse management sub-system.
The attendance management sub-system can check on the work
attendance of the employees automatically, thereby improving the
work attendance management efficiency. The employee motivation
sub-system recognizes the face expressions of the employees to
reflect the emotion states of the employees and carry out the
psychological motivation on the employees, thereby improving the
working enthusiasms. The warehouse management sub-system can
effectively avoid the people without fixed duties entering a
warehouse to steal the public goods and materials, thereby saving the
enterprise cost. The face recognition technology-based enterprise
management system fully utilizes the face image data of the
employees, and enables the enterprise management efficiency to be
improved effectively.
[0010]. CN112382051A Smart residence security system based on
block chain The invention discloses a smart residence security
system based on a block chain, and the system is characterized in
that the system comprises a sound positioning module which is used
for judging whether there is an abnormal sound or not, and achieving
the positioning of the abnormal sound; a face recognition module
used for recognizing a face and judging whether the face is a house
owner or not; a police emotion sensing module used for monitoring
whether a dangerous case occurs in the house; a networking alarm
module used for sending alarm information to residents and security
personnel when an alarm situation occurs, and providing abnormal
position information; a block chain database used for storing face
information of residents and workers in a community; and a sound
positioning module which comprises a microphone array module, a
sound acquisition module, a sound preprocessing module and an
abnormal sound positioning module. The residence security system
is more stable in operation and safer in operation.
[0011]. AU2020103514A4 IFER- Student Behavior Identification:
USING FER ABSTRACT Our Invention\" IFER- Student
Behavior Identification\" is a human Face expression Recognition is
one of the most powerful and challenging tasks in the world. The
social communication. Generally, face expressions are natural and
direct means for human Beings to communicate their emotions and
intentions. Face expressions are the key Characteristics of non-verbal
communication and this describes the survey of Face Expression
Recognition (FER) techniques which include the three major stages
such as Pre-processing, feature extraction and classification. The
invented technology also includes the survey explains the various
types of FER techniques with its major contributions. The
performance of various FER techniques Is compared based on the
number of expressions recognized and complexity of algorithms.
The IFER- student behavior identification is a method for teaching
social behavior to students comprises the steps of identifying rules of
conduct, establishing positive consequences for obeying the rules of
conduct and establishing negative consequences for disobeying the
rules of conduct, teaching the students the rules of conduct.
[0012]. US20190311188AI Face emotion recognition method based
on dual-stream convolutional neural network A face emotion
recognition method based on dual-stream convolutional neural
network uses a multi-scale face expression recognition network to
single frame face images and face sequences to perform learning
classification. The method includes constructing a multi-scale face
expression recognition network which includes a channel network
with a resolution of 224x224 and a channel network with a
resolution of 336x336, extracting facial expression characteristics at
different resolutions through the recognition network, effectively
combining static characteristics of images and dynamic
characteristics of expression sequence to perform training and
learning, fusing the two channel models, testing and obtaining a
classification effect of facial expressions. The present invention fully
utilizes the advantages of deep learning, effectively avoids the
problems of manual extraction of feature deviations and long time,
and makes the method provided by the present invention more
adaptable. Moreover, the present invention improves the accuracy
and productivity of expression recognition.
[0013]. US11010600B2 Face emotion recognition method based
on dual-stream convolutional neural network A face emotion
recognition method based on dual-stream convolutional neural
network uses a multi-scale face expression recognition network to
single frame face images and face sequences to perform learning
classification. The method includes constructing a multi-scale face
expression recognition network which includes a channel network
with a resolution of 224x224 and a channel network with a
resolution of 336x336, extracting facial expression characteristics at
different resolutions through the recognition network, effectively
combining static characteristics of images and dynamic
characteristics of expression sequence to perform training and
learning, fusing the two channel models, testing and obtaining a
classification effect of facial expressions. The present invention fully
utilizes the advantages of deep learning, effectively avoids the
problems of manual extraction of feature deviations and long time,
and makes the method provided by the present invention more
adaptable. Moreover, the present invention improves the accuracy
and productivity of expression recognition.
[0014]. CN109558535A Personalized article pushing method and
system based on face recognition The invention provides a
personalized article pushing method and system based on face
recognition, and the method comprises the steps of obtaining a face
image in a video image, recognizing the face image, and building a
user file according to a recognition result; analyzing the face image
based on the demographic information to obtain user attribute
information, and storing the user attribute information to a user file;
calculating similarity among the users, performing clustering
analysis on the user archives to extract behavior characteristics of the
users, and converting the behavior characteristics of the users to
generate a neighbor set related to the users; and generating a user
feature vector by combining the neighbor set and the user attribute
information, and constructing a feature matrix between the user
feature vector and the article from the article set to form an initial
pushing result of the article.
[0015]. CN207960434U Safety device of family based on people's
face facial expression recognition and behavior analysis The utility
model relates to a safety device of family based on people's face
facial expression recognition and behavior analysis. It including
install stealthy anti -theft net on the balcony, slip driving mechanism
and with slip driving mechanism signal connection's processing
controls ware, still including be used for gathering and people's face
facial expression recognition device of analyst's the expression
information of face with be used for gathering and the behavior
analysis device of analyst's action information, this people's face
facial expression recognition device and behavior analysis device all
with processing controls ware signal connection. The utility model
discloses combine people's face facial expression recognition device
and behavior analysis device on using the basis of stealthy anti -theft
net, possess the advantage of stealthy anti -theft net outside, obtain
assay people's emotional state with the expression of the face
through the monitoring people with action through people's face
facial expression recognition device.
[0016]. CN112370037A Safe driving method and system based on
emotion recognition The invention relates to the technical field
of intelligent aided driving, and provides a safe driving method and
system based on emotion recognition. A master control module, an
image acquisition module, a vehicle state collection module, a heart
rate collection device, a voice module and a vehicle braking module
are taken as components to establish a safe driving mechanism,
wherein the image acquisition module, the vehicle state collection
module, the heart rate collection device, the voice module and the
vehicle braking module are connected with the master control
module; the personality information of a driver is collected in
advance to obtain a personality value, the heart rate and the face
image of the driver are collected in real time, a corresponding heart
rate change rate is obtained through calculation, and the emotion of
the driver is determined through face expression recognition;
through multi-pronged approaches, accuracy for driver emotion
recognition can be improved, and before the driver emits a
dangerous driving behavior, a voice prompt is given to pacify the
emotion of the driver.
[0017]. CN109558535B Method and system for individually
pushing articles based on face recognitionThe invention provides a
personalized article pushing method and system based on face
recognition, and the method comprises the steps of obtaining a face
image in a video image, recognizing the face image, and building a
user file according to a recognition result; analyzing the face image
based on the demographic information to obtain user attribute
information, and storing the user attribute information to a user file;
calculating similarity among the users, performing clustering
analysis on the user archives to extract behavior characteristics of the
users, and converting the behavior characteristics of the users to
generate a neighbor set related to the users; and generating a user
feature vector by combining the neighbor set and the user attribute
information, and constructing a feature matrix between the user
feature vector and the article from the article set to form an initial
pushing result of the article. According to the present invention, the
data information, such as the gender, the age and the emotion of the
user and the like is obtained by analyzing the face image of the user,
[0018]. US8873813B2Application of Z-webs and Z-factors to analytics,
search engine, learning, recognition, natural language, and other
utilities Here, we introduce Z-webs, including Z-factors and Z
nodes, for the understanding of relationships between objects,
subjects, abstract ideas, concepts, or the like, including face, car,
images, people, emotions, mood, text, natural language, voice, music,
video, locations, formulas, facts, historical data, landmarks,
personalities, ownership, family, friends, love, happiness, social
behavior, voting behavior, and the like, to be used for many
applications in our life, including on the search engine, analytics, Big
Data processing, natural language processing, economy forecasting,
face recognition, dealing with reliability and certainty, medical
diagnosis, pattern recognition, object recognition, biometrics, security
analysis, risk analysis, fraud detection, satellite image analysis,
machine generated data analysis, machine learning, training samples,
extracting data or patterns (from the video, images, and the like),
editing video or images, and the like. Z-factors include reliability
factor, confidence factor, expertise factor, bias factor, and the like,
which is associated with each Z-node in the Z-web.
[0019].
[0020]. Groupings of alternative elements or embodiments of the
invention disclosed herein are not to be construed as limitations.
Each group member can be referred to and claimed individually or in
any combination with other members of the group or other elements
found herein. One or more members of a group can be included in,
or deleted from, a group for reasons of convenience and/or
patentability. When any such inclusion or deletion occurs, the
specification is herein deemed to contain the group as modified thus
fulfilling the written description of all Markus groups used in the
appended claims.
[0021]. As used in the description herein and throughout the claims that
follow, the meaning of "a," "an," and "the" includes plural reference
unless the context clearly dictates otherwise. Also, as used in the
description herein, the meaning of "in" includes "in" and "on" unless
the context clearly dictates otherwise.
[0022]. The recitation of ranges of values herein is merely intended to
serve as a shorthand method of referring individually to each separate
value falling within the range. Unless otherwise indicated herein, each
individual value is incorporated into the specification as if it were
individually recited herein. All methods described herein can be
performed in any suitable order unless otherwise indicated herein or
otherwise clearly contradicted by context.
[0023]. The use of any and all examples, or exemplary language (e.g.
"such as") provided with respect to certain embodiments herein is
intended merely to better illuminate the invention and does not pose a
limitation on the scope of the invention otherwise claimed. No
language in the specification should be construed as indicating any
non-claimed element essential to the practice of the invention.
[0024]. The above information disclosed in this Background section is
only for enhancement of understanding of the background of the
invention and therefore it may contain information that does not form
the prior art that is already known in this country to a person of
ordinary skill in the art.
[0025]. The present invention mainly cures and solves the technical
problems existing in the prior art. In response to these problems, the
present invention provides a system for productivity determination
using machine learning based face recognition.
[0026]. The present invention discloses a system for productivity
determination using machine learning based face recognition,
wherein system comprises at least one computing device and at least
one camera, wherein the computing device process the information
received from the camera through a computer implemented method
wherein computer implemented method of recording emotional
stability of a worker characterized by:
[0027]. starting the method with worker information data; Activating on
emotional stability unit on a mobile computing device with worker
information data to open the application; Determining the
Navigation information by navigation unit; activating the camera
unit; Determining the face image;
[0028]. Activating the face scanner unit; Determining the face
recognition; mothering the information through machine learning
based mothering unit and marking the emotional stability on
successful match and ending the method; wherein: on failing to
detect availability of worker information data after starting;
[0029]. logging into emotional stability unit on a mobile computing
device with Wireless communication; Determining the Navigation
information by navigation unit; Activating the camera unit;
Determining the face image; activating the face scanner unit;
[0030]. Determining the face recognition & transferring the data through
local wireless communication unit and perform mothering the
information through machine learning based computing unit and
marking the emotional stability on successful match and ending the
method.
[0031]. The principle objective of the present invention is to provide a
system for productivity determination using machine learning based
face recognition.
[0032]. Further clarify various aspects of some example embodiments of
the present invention, a more particular description of the invention
will be rendered by reference to specific embodiments thereof which
are illustrated in the appended drawings. It is appreciated that these
drawings depict only illustrated embodiments of the invention and are
therefore not to be considered limiting of its scope. The invention will
be described and explained with additional specificity and detail
through the use of the accompanying drawings.
[0033]. In order that the advantages of the present invention will be
easily understood, a detailed description of the invention is discussed
below in conjunction with the appended drawings, which, however,
should not be considered to limit the scope of the invention to the
accompanying drawings, in which:
[0034]. Figure 1 shows an exemplary representation of a system for
productivity determination using machine learning based face
recognition, according to the present invention.
[0035]. The present invention discloses a system for productivity
determination using machine learning based face recognition.
[0036]. Figure 1 shows the exemplary representation of a system for
productivity determination using machine learning based face
recognition, according to the present invention.
[0037]. Although the present disclosure has been described with the
purpose of to a system for productivity determination using machine
learning based face recognition, it should be appreciated that the same
has been done merely to illustrate the invention in an exemplary
manner and to highlight any other purpose or function for which
explained structures or configurations could be used and is covered
within the scope of the present disclosure.
[001]. The present invention discloses a system for
productivity determination using machine learning
based face recognition, wherein system comprises at
least one computing device and at least one camera,
wherein the computing device process the information
received from the camera through a computer
implemented method wherein computer implemented
method of recording emotional stability of a worker
characterized by:
[002]. starting the method with worker information data; Activating on
emotional stability unit on a mobile computing device with worker
information data to open the application; Determining the
Navigation information by navigation unit; activating the camera
unit; Determining the face image;
[003]. Activating the face scanner unit; Determining the face
recognition; mothering the information through machine learning
based mothering unit and marking the emotional stability on
successful match and ending the method; wherein: on failing to
detect availability of worker information data after starting;
[004]. logging into emotional stability unit on a mobile computing
device with Wireless communication; Determining the Navigation
information by navigation unit; Activating the camera unit;
Determining the face image; activating the face scanner unit;
[005]. Determining the face recognition & transferring the data through
local wireless communication unit and perform mothering the
information through machine learning based computing unit and
marking the emotional stability on successful match and ending the
method.
[006]. The figures and the foregoing description give examples of
embodiments. Those skilled in the art will appreciate that one or
more of the described elements may well be combined into a single
functional element. Alternatively, certain elements may be split into
multiple functional elements. Elements from one embodiment may
be added to another embodiment. For example, order of processes
described herein may be changed and are not limited to the manner
described herein. Moreover, the actions of any block diagram need
not be implemented in the order shown; nor do all of the acts need to
be necessarily performed. Also, those acts that are not dependent on
other acts may be performed in parallel with the other acts. The
scope of embodiments is by no means limited by these specific
examples.
[007]. Although implementations of the invention have been described
in a language specific to structural features and/or methods, it is to
be understood that the appended claims are not necessarily limited to
the specific features or methods described. Rather, the specific
features and methods are disclosed as examples of implementations
of the invention.
Claims (1)
1. A system for productivity determination using
machine learning based face recognition, wherein
system comprises at least one computing device and
at least one camera, wherein the computing device
process the information received from the camera
through a computer implemented method wherein
computer implemented method of recording
emotional stability of a worker characterized by:
starting the method with worker information data;
Activating on emotional stability unit on a mobile
computing device with worker information data to
open the application;
Determining the Navigation information by
navigation unit; activating the camera unit;
Determining the face image;
Activating the face scanner unit;
Determining the face recognition;
mothering the information through machine learning
based mothering unit and marking the emotional
stability on successful match and ending the method;
wherein: on failing to detect availability of worker
information data after starting;
logging into emotional stability unit on a mobile
computing device with Wireless communication;
Determining the Navigation information by
navigation unit;
Activating the camera unit;
Determining the face image;
activating the face scanner unit; Determining the
face recognition & transferring the data through
local wireless communication unit and perform
mothering the information through machine learning based computing unit and marking the emotional stability on successful match and ending the method.
FIGURE 1 DRAWINGS
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021103335A AU2021103335A4 (en) | 2021-06-14 | 2021-06-14 | System for productivity determination using machine learning based face recognition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021103335A AU2021103335A4 (en) | 2021-06-14 | 2021-06-14 | System for productivity determination using machine learning based face recognition |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2021103335A4 true AU2021103335A4 (en) | 2022-03-31 |
Family
ID=80855670
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2021103335A Ceased AU2021103335A4 (en) | 2021-06-14 | 2021-06-14 | System for productivity determination using machine learning based face recognition |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2021103335A4 (en) |
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2021
- 2021-06-14 AU AU2021103335A patent/AU2021103335A4/en not_active Ceased
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