RESEARCH_PAPER_V2.1

The Neural Core

A scientific deep-dive into our biometric resolution pipeline and high-dimensional identity mapping.

01 // Abstract

Our system implements a multi-stage convolutional and transformer-based pipeline designed for real-time identity resolution and attribute inference. By leveraging state-of-the-art architectures such as FaceNet512 and MiVOLO, we project raw pixel data into a 512-dimensional manifold where semantic similarity correlates with geometric proximity.

02 // Face Detection & Alignment

The ingestion phase begins with the YuNet detector, an extremely efficient and lightweight face detection model. YuNet allows us to isolate facial regions with high confidence even in varying lighting conditions.

PRE-PROCESSING_PROTOCOL

Face Extraction: Dynamic bounding box calculation based on confidence scores.
Custom Normalization: Each frame is resized to 160x160 pixels and subjected to local variance normalization.
Spatial Correction: Alignment based on facial landmarks to ensure consistent orientation.

03 // Identity Embeddings (FaceNet512)

To resolve identity, we employ FaceNet512. Unlike traditional classification models, FaceNet uses a triplet loss function during training to learn a mapping directly from face images to a compact Euclidean space.

In this 512-dimensional space, the distance between embeddings corresponds to face similarity. Two images of the same person will have a small L2 distance, while images of different people will be further apart.

d(x, y) = ||f(x) - f(y)||_2

VECTOR_SPACE 512D High-Dimensional Manifold

04 // Attribute Inference (MiVOLO)

For Age and Gender estimation, we utilize MiVOLO (Multi-input Transformer for Age and Gender Estimation). MiVOLO is a state-of-the-art Vision Outlooker (VOLO) based model that can process both face and body information to increase accuracy.

AGE_REGRESSION

MiVOLO treats age as a continuous regression task, providing a precise estimate rather than broad buckets.

GENDER_CLASSIFICATION

Dual-class probability distribution optimized through transformer-based attention mechanisms.

05 // Sentiment Analysis

Emotions are classified using a multi-class neural network that analyzes micro-expressions. The output is a probability vector across seven primary emotions: angry, disgust, fear, happy, sad, surprise, and neutral.

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