AI & Machine Learning
Open Source

Ensemble Facial Recognition & ONNX Pipeline

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PythonTensorFlow / KerasScikit-LearnONNXskimageTkinter

A heterogeneous Machine Learning pipeline that aggregates SVM, Random Forest, and CNN architectures via soft-voting to classify facial datasets, featuring automated ONNX graph serialization.

Tech Stack

PythonTensorFlow / KerasScikit-LearnONNXskimageTkinter

System Metrics

▹Significantly reduces spatial overfitting by aggregating high-bias traditional classifiers with deep spatial feature extractors
▹ONNX serialization eliminates the heavy TensorFlow runtime requirement for downstream inference applications

Why Did I Build This?

"This pipeline was explicitly designed as the downstream classification engine for the facial datasets extracted by my `OpenCV-Face-Features-Toolkit-For-ML-cpp` project. Relying on a single ML architecture for facial recognition often leads to spatial overfitting or variance issues. I engineered this ensemble approach to mitigate individual model biases by mathematically aggregating predictions from both traditional statistical models (SVM/RF) and deep spatial feature extractors (CNN)."

Architecture & Decisions

The ingestion layer normalizes input matrices to 100x100 grayscale arrays. Because Scikit-Learn and TensorFlow expect different tensor geometries (1D flattened arrays vs. 2D spatial tensors), I implemented a custom `KerasClassifierWrapper` inheriting from `BaseEstimator` and `ClassifierMixin`. This seamlessly injects the TF/Keras computational graph into a Scikit-Learn `VotingClassifier`. The ensemble computes probability distributions (`predict_proba`) via a soft-voting mechanism. Finally, the models are serialized into ONNX format (opsets 9 and 11) to entirely decouple the inference engine from the Python/TensorFlow runtime for potential C++ edge deployment.

Key Features

  • 01.Direct ingestion and classification of normalized datasets generated by the `OpenCV-Face-Features-Toolkit-For-ML-cpp` utility
  • 02.Custom `BaseEstimator` bridging layer to orchestrate TensorFlow CNNs within Scikit-Learn ensemble pipelines
  • 03.Soft-voting ensemble classifier calculating maximum likelihood across SVM, Random Forest, and CNN probability distributions
  • 04.Automated ONNX graph serialization pipeline for cross-platform, runtime-agnostic inference