deep learning facial r ecognition system - panasonic...2019/07/16 · a facial recognition system...

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Deep Learning Facial R

Panasonic Video Surveillance S

WHITE PAPER



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Deep Learning Facial Recognition System

Panasonic Video Surveillance System

ecognition System

ystems

ecognition System

ecognition System

Table of contents

1. Introduction .......................................................................................................... 3

2. Deep Learning Facial Recognition Technology ..................................................... 3

3. Panasonic's Facial Recognition System ................................................................. 4

3.1. High precision .................................................................................................... 5

3.2. Cost Reduction .................................................................................................. 6

3.3. System expandability .......................................................................................... 6

3.4. Data protection………………………………………………………………………………………………………….7

4. Conclusion ............................................................................................................. 7

1. Introduction

Increasing attention is now being given to the monitoring of airports, train stations,

shopping malls, and other important facilities where large numbers of people

gather. In the security industry, there is a growing need for monitoring systems that

can help to stop crimes before they happen. Rather than using high resolution

cameras to passively capture people’s faces and behaviors, there is a growing need

to identify important and suspicious people to provide an active security response.

In order to solve problems such as these, Panasonic has newly developed FacePRO,

a facial recognition system which utilizes deep learning technology. With inbuilt

analytics changing traditional monitoring, the system is better equipped to provide

safer and more secure environments.

2. Deep Learning Facial Recognition Technology

With the Internet of Things (IoT) and Artificial Intelligence (AI) becoming popular

concepts in today’s society, industries are utilizing the ability to link together a

variety of information to make workflows and people’s lives easier. To achieve this,

one of the technologies being applied in each field is the new technology known as

“deep learning.”

In a joint effort with the National University of Singapore, Panasonic has engaged

in research and development that has led to the creation of an original algorithm

combining the machine learning method known as “deep learning” with a similarity

calculation method that suppresses errors. This algorithm has now been put to

practical use in our facial recognition system. As a result, we have been able to

improve the matching failure problems that have plagued conventional facial

recognition technology in cases where faces are angled, changed by aging, or

partially hidden by sunglasses, etc. This success in raising facial recognition accuracy

makes it possible to expand the situations in which facial recognition systems can

be used.

*1 In April 2017, the product achieved the highest leve

(IJB-A Face

NIST (National Institute of Standards and Technology) of the United

in the world.

3. Panasonic

3.1 High precision

The facial recognition software employs a face matching performance engine

that utilizes

University of Singapore. It has been rated at the highest level in the NIST (National

Institute of Standards and Technology) IJB

accurately face matching in s

conventional techniques such as faces that are angled (up to 45 degrees to the left

or right or 30 degrees up or down), changed by aging, or partially hidden by

sunglasses.

NIST (IJB

In April 2017, the product achieved the highest leve

A Face Verification Challenge Performance Report/IJB


in the world.

Panasonic’s Facial Recognition

High precision


utilizes deep learning technology jointly developed with the National



accurately face matching in s



sunglasses.

Registeredimage

Matching image

(IJB-A) competition : Competition between corporate and universitie

In April 2017, the product achieved the highest leve

Verification Challenge Performance Report/IJB


s Facial Recognition

High precision


deep learning technology jointly developed with the National



accurately face matching in situations that were difficult to handle with



Registered

A) competition : Competition between corporate and universitie

Feature quantification using deep learning

In April 2017, the product achieved the highest level of facial

Verification Challenge Performance Report/IJB


s Facial Recognition




Institute of Standards and Technology) IJB-A face challenge

ituations that were difficult to handle with




(unlike FRVT or FIVE)

Feature quantificationusing deep learning

ial recognition performance in

Verification Challenge Performance Report/IJB -A Face Identification Challenge Performance Report) of

NIST (National Institute of Standards and Technology) of the United States, one of the most authoritative institutes

s Facial Recognition System




A face challenge




Feature quantities

(registered image)

Feature quantities

(matching image)


(unlike FRVT or FIVE)

Feature quantification using deep learning

recognition performance in the world in a comparison test

A Face Identification Challenge Performance Report) of

States, one of the most authoritative institutes

System




A face challenge*1

, with the system




Feature quantities

(registered image)

Feature quantities

(matching image)

Sim

ilarity

calcu

latio

n

A) competition : Competition between corporate and universities to compete latest technology

the world in a comparison test






, with the system




Iden

tity ju

dgm

en

t

s to compete latest technology

the world in a comparison test





, with the system



s to compete latest technology

*2 The Best Shot License Key must be installed on the

*3 From receiving the face thumbnail to matching

*4 The best case when the time range is narrowed down and the number of search result is 100 faces. Depending on the

search conditions, the search time may take several minutes.

By using the intelligent auto( iA) and Best Shot

i-PRO Extreme cameras, it is also possible to

performance and provide high recognition precision.

（iA function

This function enables cameras to automatically

optimize

The camera detects the moving objects, movement speed, faces, and light

intensity found in video that is us

and backlight, and it

optimal video of the subject.

(Best Shot function

This function automatically selects several images suitable for facial

recognition

front of the camera, and it sends only those selected images to the server.

This enables high

without putting a load on the network.

With these functions, high

with a maximum of 30,000 faces registered) and high

data for 5 million faces in 3 seconds

Angled F

Left/Right ±45°Up/down ±30°

The Best Shot License Key must be installed on the

From receiving the face thumbnail to matching

The best case when the time range is narrowed down and the number of search result is 100 faces. Depending on the



PRO Extreme cameras, it is also possible to


iA function）


optimize the settings accordingly to improve the clarity of the video images.



and backlight, and it


(Best Shot function


recognition from the multiple face images captured when a person passes in


This enables high


With these functions, high



Angled Face Left/Right ±45° Up/down ±30°

The Best Shot License Key must be installed on the

From receiving the face thumbnail to matching







the settings accordingly to improve the clarity of the video images.



and backlight, and it optimizes


(Best Shot function*2

)


from the multiple face images captured when a person passes in


This enables high-quality images suitable for facial recognition to be sent


With these functions, high-speed matching (matching in approx. 1 second



Sunglasses

The Best Shot License Key must be installed on the i-PRO Extreme camera

From receiving the face thumbnail to matching server









intensity found in video that is usually hard to see due to subject movement

optimizes the settings in real





quality images suitable for facial recognition to be sent


speed matching (matching in approx. 1 second


data for 5 million faces in 3 seconds*4

) can be

Sunglasses Surgical M

PRO Extreme camera



By using the intelligent auto( iA) and Best Shot*2

function

PRO Extreme cameras, it is also possible to maximize





ually hard to see due to subject movement

the settings in real







with a maximum of 30,000 faces registered) and high-speed searching (searching

) can be realized.

Surgical Mask

Facial Recognition Server

Improve face match processing speed


function installed on Panasonic

maximize facial recognition engine

This function enables cameras to automatically recognize




the settings in real-time to capture more






speed searching (searching

.

Changed by A




installed on Panasonic

facial recognition engine

the scene and




time to capture more







Changed by A




installed on Panasonic

facial recognition engine

the scene and




time to capture more




speed matching (matching in approx. 1 second*3

even


Changed by Aging


Improve face match processing speedImprove face match processing speed

3.2 Cost Reduction

With conventional facial recognition systems, all captured images are sent to the

server and the server performs face detection and recognition resulting in the

processing load being concentrated on the server. Systems also tend to be

large-scale because of the large bandwidth required to send all the images and the

large volume of hard drive space required to save those images.

To provide a solution to data storage, Panasonic’s facial recognition system uses

the Best Shot function to only send Best Shot images to the server, thereby making

it possible to eliminate the need for wide bandwidth and reduce the network load.

In addition, performing facial recognition on the server using the Best Shot images

reduces server load and hard drive volume usage so up to 20*5

cameras can be

connected to a single server.

3.3 System expandability

・Simple batch registration of a maximum of 10,000 faces is possible with the

standard Facial Recognition Server Software (WV-ASF950). The optional Face

Registration Expansion Kit (WV-ASFE951W) enables registration of up to 30,000

faces at large-scale facilities.

・Face detection, face matching, and tracking with recorded video can be performed

in the same GUI by integrating management with the WV-ASM300 or

WV-ASE231W client software for Panasonic i-PRO monitoring systems. This

eliminates the need for the dedicated operation and management required for

conventional facial recognition systems and enables work to be centralized.

*5 The number of cameras that can be connected depends on the number of people passing by the camera and the retention

period for face images.

・Additional

The face recognition accuracy improved by the deep learning techn

be used for unregistered face detec

In areas where outsiders and suspicious persons are not allowed to enter such as

the backyard of a store and company's development

persons other than registered faces with high accuracy and to notify an alert to field

surveillance

As a system configuration, i

maximum 10 cameras can be connected in a face matching server.

*6

Detect unregistered faces in approx. 3 second even with a maximum of 30,000 faces registered depending of setting

Unregistered

3.4 Data protection

Whilst working to increase the accuracy of facial recognition, Panasonic is also

providing

security of important data

as a whole.

・Can be selected to store facial

・Face thumbnail in DB will be encrypted.

・SSL communication(Camera

・Registered faces

・Face matching

・Retention period and number of face data

・Log data for all the operation can be saved.

Additional Unregistered






surveillance can be reinforce

As a system configuration, i



Unregistered Face Detection and Registered Face

3.4 Data protection

working to increase the accuracy of facial recognition, Panasonic is also

capabilities such as

security of important data

as a whole.

Can be selected to store facial

Face thumbnail in DB will be encrypted.

SSL communication(Camera

*Face cannot

egistered faces

ace matching data can be deleted

etention period and number of face data

Log data for all the operation can be saved.

Unregistered Face






can be reinforced.、

As a system configuration, in the case of unregistered face detection



Face Detection and Registered Face

3.4 Data protection


capabilities such as the ones listed

security of important data, and therefore

Can be selected to store facial


SSL communication(Camera

Face cannot be reproduce

egistered faces can be deleted

data can be deleted



ace Detection


be used for unregistered face detection as well as registered face re




、

n the case of unregistered face detection



Face Detection and Registered Face Detection simultaneously.


the ones listed

therefore build safety management into the system

Can be selected to store facial data.


SSL communication(Camera-Server-Client)

reproduced from Metadata.

can be deleted after valid

data can be deleted after



etection setting


tion as well as registered face re

Detect unregistered person


the backyard of a store and company's development department, it is detect





Detection simultaneously.


the ones listed below to strengthen the information

build safety management into the system


Client)

from Metadata.

after valid time frame

after valid time frame

etention period and number of face data collected can be set



tion as well as registered face re



department, it is detect






below to strengthen the information


from Metadata.

time frame.

valid time frame.

collected can be set

The face recognition accuracy improved by the deep learning technology can

tion as well as registered face recognition.



department, it is detect


n the case of unregistered face detection setting,






collected can be set.

ology can

cognition.

Detect unregistered persons


department, it is detect*6


setting,





4. Conclusion

By utilizing deep learning technology, Panasonics facial recognition system makes it

possible to build a high-precision facial recognition and video monitoring system

that can perform more complex and sophisticated monitoring centered on people.

Furthermore, by cutting data volumes, this technology will also contribute to

reductions in transmission, network-construction, and operation costs.

deep learning facial r ecognition system - panasonic...2019/07/16 · a facial recognition system...

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