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Face Recognition Solution for Retail Industry

MNDA

SME
Company Type
Sydney, Australia
Location
Project work
Engagement Model
6 - 10 people
Team Size
6 - 9 Months
Duration
$200K - $250K
Budget

About

Innowise developed a custom face recognition software solution for the retail industry to enhance security and streamline identification processes. Our client is an Australian software development and service provider company specializing in IT solutions for the retail industry. This company has an impressive track record of conceptualizing, designing, developing, and launching a range of innovative retail services catering to various products.

Challenge, approach, and impact

Development of Cutting-Edge Facial Recognition for Retail

Creating a scalable and distributed architecture employing various algorithms for accurate face recognition and face training, with the ability to extract faces from live video feeds for real-time capabilities.

Custom Facial Recognition Software Tailored for Retail Industry

Development of a custom facial recognition software with diverse face recognition algorithms, efficient batch image processing, enhancements for accuracy and performance of images, integration with a Closed-Circuit Television (CCTV) system, and a fully-customizable face recognition system.

DIVERSE FACE RECOGNITION ALGORITHMS

We implemented a collection of face-recognition modules featuring different algorithms, such as Unmanaged Face Recognition PCA, Managed Face Recognition PCA, and Managed Face Recognition Eigenfaces. These modules were seamlessly interchangeable, offering flexibility and adaptability to meet the specific requirements.

EFFICIENT BATCH IMAGE PROCESSING

To simplify image processing tasks, we integrated a batch image processing module. This powerful tool allowed us to extract images from image series, videos, or cameras efficiently. It significantly saved time and effort, enabling smooth operations even when handling large volumes of data.

ENHANCEMENTS FOR ACCURACY AND PERFORMANCE OF IMAGES

We focused on improving the accuracy and performance of our system, particularly for images with inferior quality. By leveraging two OpenCV algorithms for face detection and eye localization, we achieved stable and reliable face detection.

How we built

Video Streaming
Web Apps
IoT & Smart Devices
Enterprise Software
Integrations

Testimonials

Anonymous

Innowise Sp. z o.o

Verified Testimonial

As Business Analyst on this facial recognition project, I'm proud of Innowise's delivery. We tackled inconsistent video quality/lighting challenges, achieving 80% face identification accuracy and 75% faster authentication. Scrum with Jira/Confluence/Teams ensured client alignment. CCTV integration enabled real-time tracking/alerts across retail environments. Transformed security with scalable, robust face training + live extraction. Outstanding computer vision expertise!

Team structure

Client team

Jack's avatar

Jack

Project Manager

Daily point of contact

The client stakeholders were working closely with the team at Innowise Sp. z o.o

Agency team

1 x Business Analyst's avatar

1 x Business Analyst

Production

1 x Project Manager's avatar

1 x Project Manager

Production

1 x Data Scientist's avatar

1 x Data Scientist

Production

1 x QA's avatar

1 x QA

Production

1 x Backend Developer's avatar

1 x Backend Developer

Production

1 x Frontend Developer's avatar

1 x Frontend Developer

Production

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