Facial Emotion Recognition

Business challenges

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

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

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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.

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

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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.

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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.

Solution Overview

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

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

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

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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.

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

Business Impact

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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.

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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.

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

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

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

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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.

Key Features

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Configure IP cameras and feed video data to AWS cloud.
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Stream the video via AWS Kinesis.
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Use Recognition to detect faces to inform the next step (no faces, no action).
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Lambda functions orchestrate multiple Recognition services to determine the visitor’s gender, emotions, and age.
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Lambda functions store the derived data to the RDS
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The derived data is sent to the appropriate stores as messages.
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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

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50+

AI & ML
Engineers

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40+

AI & ML
Projects for
reputed Clients

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5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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