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Facial Analytics

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Introduction​

  • Definition: Analyze the facial features like age, gender, emotion, and identity.
  • Applications: Identity verification, emotion detection
  • Scope: Human faces only, Real-time
  • Tools: OpenCV, dlib

Models​

FaceNet​

FaceNet: A Unified Embedding for Face Recognition and Clustering. CVPR, 2015.

RetinaFace​

RetinaFace: Single-stage Dense Face Localisation in the Wild. arXiv, 2019.

FER+​

Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution. arXiv, 2016.

Process flow​

Step 1: Collect Images

Capture via camera, scrap from the internet or use public datasets

Step 2: Create Labels

Compile a metadata table containing a unique id (preferably the same as the image name) for each face id.

Step 3: Data Preparation

Setup the database connection and fetch the data into the environment. Explore the data, validate it, and create a preprocessing strategy. Clean the data and make it ready for modeling

Step 4: Model Building

Create the model architecture in python and perform a sanity check. Start the training process and track the progress and experiments. Validate the final set of models and select/assemble the final model

Step 5: UAT Testing

Wrap the model inference engine in API for client testing

Step 6: Deployment

Deploy the model on cloud or edge as per the requirement

Step 7: Documentation

Prepare the documentation and transfer all assets to the client

Use Cases​

Automatic Attendance System via Webcam​

We use Face Recognition library and OpenCV to create a real-time webcam-based attendance system that will automatically recognizes the face and log an attendance into the excel sheet. Check out this notion.

Detectron2 Fine-tuning for face detection​

Fine-tuned detectron2 on human face dataset to detect the faces in images and videos. Check out this notion.