Facial Emotion Recognition

Business challenges

Lack of customer data: Retail owners may not have access to enough customer data to make informed decisions about customer behavior.

Difficulty in measuring customer behavior: Retail owners may struggle to measure customer behavior in a meaningful way that accurately reflects their shopping patterns.

Personalization challenges: Retail owners must personalize their offerings and experiences for each customer, which can be difficult given the sheer number of customers they serve.

Understanding changing customer preferences: Retail owners must keep up with changing customer preferences and evolve their offerings accordingly.

Balancing in-store and online experiences: Retail owners must balance the needs of customers who shop in-store and online, which can be a complex challenge.

Adapting to market trends: Retail owners must adapt to market trends and changing customer behaviors, which can be challenging in an ever-evolving retail landscape.

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Solution Overview

Optisol developed a subscription-based web application that can recognize a visitor's face, age, gender, and mood in retail stores.

The customer aimed to improve the in-store closure rate by reading customers' moods, leading to increased revenue.

The implementation of ML based solution on live-streamed video took 3 months and provided valuable insights to store staff.

With appropriate training, store staff could approach shoppers with tailored narratives based on age and mood, resulting in a 59% increase in the closure rate.

The store owner used insights from the application to gauge customers' sentiment and make incremental changes to the store.

Business Impact

Improved customer engagement: By understanding the age, gender, and mood of customers, store staff can tailor their approach and engage with customers in a more personalized and effective way.

Increased sales: By approaching customers with tailored narratives based on their mood and age, store staff can increase the in-store closure rate and drive sales.

Better customer experience: Understanding customer mood and tailoring experiences can lead to a better overall customer experience, resulting in increased customer satisfaction and loyalty.

Improved decision making: Store owners can use insights generated by the ML solution to make informed decisions about store operations and customer engagement.

Increased efficiency: The ML solution can automate the process of identifying customer mood, freeing up staff time and resources to focus on other tasks.

Competitive advantage: Implementing cutting-edge technology such as an ML based mood recognition solution can set a retail store apart from its competitors and give it a competitive advantage.

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Key Features

Configure IP cameras and feed video data to AWS cloud.

Stream the video via AWS Kinesis.

Use Recognition to detect faces to inform the next step (no faces, no action).

Lambda functions orchestrate multiple Recognition services to determine the visitor’s gender, emotions, and age.

Lambda functions store the derived data to the RDS

The derived data is sent to the appropriate stores as messages.

The in-store application displayed the streaming video with the derived metadata for the store staff to see.

Architecture Diagram

Trusted and Proven Engagement Model

A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
Our NDA-driven process is established to keep clients’ data and IP safe and secure.

The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
This helps clarify the goals, and limitations, and deliver quality products & services.

Our engagement model defines the project size, project development plan, duration, concept, POC etc.
Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).

The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.

On signing the SOW, an official project kick-off meeting shall be initiated.
Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

50+

AI & ML
Engineers

40+

AI & ML
Projects for
reputed Clients

5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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