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Proof-of-concept iOS App for Omron

Showcasing okao vision face sensing technology with real-time image processing

  • iOS
  • Computer Vision
Solution

iOS app based on OKAO Vision Face Sensing Technology

Industry Software and Technology
Engagement Model

Fixed Price

Methodology Waterfall
Team
  • iOS Software Engineers
  • QA Engineers
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Customer

Problem

To better showcase its proprietary face recognition technology to prospects, Omron realized a demo mobile app would be a viable option. Faced with insufficient in-house resource availability, the company turned to Softeq on a customer recommendation of its broad mobile technology competency and strong embedded software engineering background.

Softeq was to deliver an iOS app to demo Omron’s OKAO Vision Face Sensing Technology capabilities by using the respective SDK. The app was expected to replicate the features implemented in the Android app version.

Solution

The Android demo app lacked technical specification. The team had to perform a close analysis of the app’s functionality to understand how it could be mapped to iOS. The delivered app demonstrates the following features of the OKAO Vision Face Sensing Technology:

  • Face Detection: Quickly and accurately locating multiple faces in a target image.
  • Facial Feature Extraction: Extracting exact face feature position (e.g. eyebrow, eye, nose, mouth and face contour) from the target image.
  • Face Recognition: Identifying a person by comparing his/her face with face images stored in the database.
  • Analysis of Facial Characteristics: Analyzing person’s facial characteristics such as gender, age and ethnicity.
  • Automatic Optimum Facial Picture Adjustment: Automatically enhancing person’s complexion in the target image.

Challenge

Matching a scanned image against the images stored in the database in real-time would require using the device’s CPU to its full capacity, which would still be insufficient.

The team developed the app so that it also tapped the computing power of the GPU core by employing shaders, pieces of code executed on the GPU. This allowed faster image processing time, which was critical for previous models of iPhone with low battery capacity.

Results

Softeq’s team demonstrated excellent analytical skills in nailing the customer’s requirements and delivering a powerful iOS proof-of-concept app, which even functionally outperformed its Android counterpart.

Omron appreciated the team’s development speed and deliverables quality, emphasizing the overall efficiency of the implementation initiative carried out by Softeq.