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FaceVector Engine - Technical Details

This document provides an in-depth technical explanation of how the FaceVector Engine works internally, including image processing pipelines, coordinate transformations, and model inference workflows.

Table of Contents

  1. System Architecture Overview
  2. Image Processing Pipeline
  3. Coordinate Transformation System
  4. Model Inference Details
  5. Storage Architecture
  6. API Workflow Deep Dive
  7. Performance Optimizations

System Architecture Overview

High-Level Components

┌─────────────────────────────────────────────────────────────┐
│                      Client Application                     │
└───────────────────────────┬─────────────────────────────────┘
                            │ HTTP/REST API
┌───────────────────────────▼─────────────────────────────────┐
│                   Express API Server                        │
│  ┌────────────┐  ┌────────────┐  ┌─────────────────────┐    │
│  │Controllers │  │ Services   │  │ Middleware (Multer) │    │
│  └────────────┘  └────────────┘  └─────────────────────┘    │
└────────┬───────────────────┬─────────────────┬──────────────┘
         │                   │                 │
         │   ┌───────────────▼──────────┐      │
         │   │   ONNX Runtime Node      │      │
         │   │  ┌──────────────────┐    │      │
         │   │  │ RetinaFace (840) │    │      │
         │   │  └──────────────────┘    │      │
         │   │  ┌──────────────────┐    │      │
         │   │  │ ArcFace (112)    │    │      │
         │   │  └──────────────────┘    │      │
         │   └──────────────────────────┘      │
         │                                     │
┌────────▼────────────┐              ┌─────────▼──────────┐
│   PostgreSQL +      │              │                    │
│    pgvector         │              │   MinIO S3         │
│  ┌────────────────┐ │              │  ┌──────────────┐  │
│  │ detected_faces │ │              │  │originals/    │  │
│  └────────────────┘ │              │  └──────────────┘  │
│  ┌────────────────┐ │              │  ┌──────────────┐  │
│  │enrolled_       │ │              │  │faces/        │  │
│  │  customers     │ │              │  └──────────────┘  │
│  └────────────────┘ │              └────────────────────┘
└─────────────────────┘

Component Responsibilities

Component Responsibility Key Files
Controllers Handle HTTP requests/responses src/controllers/*.ts
Services Business logic & orchestration src/services/*.ts
Utils Image processing & transformations src/utils/*.ts
Models ONNX model inference src/embedding.ts, src/retinaface.ts
Database Vector storage & search src/db.ts
Storage S3 object storage src/services/s3Service.ts

Image Processing Pipeline

Complete Flow: Upload → Storage

┌─────────────────────────────────────────────────────────────────┐
│ STAGE 1: File Upload & Initial Processing                       │
└─────────────────────────────────────────────────────────────────┘

User uploads image (e.g., 3000 x 2000 pixels, JPEG)
         │
         ▼
┌────────────────────────────────────────────────────────────────┐
│ Multer Middleware (src/middleware/upload.ts)                   │
│ - Validates file type (PNG, JPG, WEBP)                         │
│ - Checks file size (max 10MB)                                  │
│ - Stores in memory as Buffer                                   │
└────────┬───────────────────────────────────────────────────────┘
         │
         ▼
┌────────────────────────────────────────────────────────────────┐
│ scaleDownImage() - src/utils/imageUtils.ts:74-91               │
│                                                                │
│ Input:  Buffer (3000 x 2000)                                   │
│ Logic:  if (width > 1920 OR height > 1920) {                   │
│           scale to max 1920px (maintains aspect ratio)         │
│         }                                                      │
│ Output: Base64 string (1920 x 1280)                            │
│                                                                │
│ Why: Performance optimization - reduces processing time        │
└────────┬───────────────────────────────────────────────────────┘
         │
         ▼ base64 (1920 x 1280)
         │
┌────────────────────────────────────────────────────────────────┐
│ STAGE 2: Face Detection with RetinaFace                        │
└────────────────────────────────────────────────────────────────┘

detectAllFacesWithRetinaFace(base64, visThreshold)
         │
         ▼
┌────────────────────────────────────────────────────────────────┐
│ preprocessForRetinaFace() - src/retinaface.ts:202-232          │
│                                                                │
│ 1. Load image to Jimp:                                         │
│    const image = await base64ToJimp(base64)                    │
│    originalWidth = 1920 ← PRESERVED                            │
│    originalHeight = 1280 ← PRESERVED                           │
│                                                                │
│ 2. Resize for model input:                                     │
│    await image.resize({ w: 840, h: 840 })                      │
│                                                                │
│ 3. Extract RGB and subtract mean:                              │
│    meanValues = [104, 117, 123]                                │
│    Convert to CHW format (Channels, Height, Width)             │
│                                                                │
│ Output: {                                                      │
│   data: Float32Array (840 x 840 x 3),                          │
│   originalWidth: 1920,  ← KEY: Used for scaling back!          │
│   originalHeight: 1280                                         │
│ }                                                              │
└────────┬───────────────────────────────────────────────────────┘
         │
         ▼
┌────────────────────────────────────────────────────────────────┐
│ RetinaFace Inference - src/retinaface.ts:473-500               │
│                                                                │
│ 1. Run ONNX model on 840x840 tensor                            │
│    const outputs = await retinaSession.run({ input0 })         │
│                                                                │
│ 2. Decode bounding boxes and landmarks                         │
│    boxes = decode(locArray, priors, variance)                  │
│    landmarks = decodeLandmarks(landmsArray, priors, variance)  │
│                                                                │
│ 3. Scale to pixel coordinates using ORIGINAL dimensions        │
│    const scale = [originalWidth, originalHeight, ...]          │
│    scaledBoxes = boxes.map(box => [                            │
│      box[0] * 1920,  // x1 in original image                   │
│      box[1] * 1280,  // y1 in original image                   │
│      box[2] * 1920,  // x2 in original image                   │
│      box[3] * 1280   // y2 in original image                   │
│    ])                                                          │
│                                                                │
│ 4. Apply NMS (Non-Maximum Suppression)                         │
│    Filter overlapping boxes (threshold: 0.4)                   │
│                                                                │
│ 5. Filter by confidence (threshold: 0.8)                       │
│                                                                │
│ Output: DetectedFace[] with coordinates in 1920x1280 space     │
│         [{                                                     │
│           PixelBoundingBox: { Left: 450, Top: 300,             │
│                               Width: 600, Height: 850 },       │
│           Confidence: 99.8,                                    │
│           Landmarks: [{ eyeLeft, eyeRight, ... }]              │
│         }]                                                     │
└────────┬───────────────────────────────────────────────────────┘
         │
         ▼
┌────────────────────────────────────────────────────────────────┐
│ STAGE 3: Storage - src/services/faceDetectionService.ts        │
└────────────────────────────────────────────────────────────────┘

For EACH detected face:
         │
         ▼
┌────────────────────────────────────────────────────────────────┐
│ 1. Store Original Image to S3                                  │
│    originalImageId = randomUUID()                              │
│    s3Service.uploadImage(                                      │
│      key: "originals/{uuid}.jpg",                              │
│      buffer: base64ToBuffer(1920x1280 image)                   │
│    )                                                           │
└────────┬───────────────────────────────────────────────────────┘
         │
         ▼
┌────────────────────────────────────────────────────────────────┐
│ 2. Crop Face Region - src/utils/imageUtils.ts:44-65            │
│    cropImageRegion(                                            │
│      base64: 1920x1280 original,                               │
│      x: 450, y: 300, width: 600, height: 850                   │
│    )                                                           │
│    ↓                                                           │
│    Extracts 600x850 region from original image                 │
│    Returns: base64 (600 x 850)                                 │
└────────┬───────────────────────────────────────────────────────┘
         │
         ▼
┌────────────────────────────────────────────────────────────────┐
│ 3. Store Cropped Face to S3                                    │
│    faceId = randomUUID()                                       │
│    s3Service.uploadImage(                                      │
│      key: "faces/{face_id}.jpg",                               │
│      buffer: base64ToBuffer(600x850 cropped face)              │
│    )                                                           │
└────────┬───────────────────────────────────────────────────────┘
         │
         ▼
┌────────────────────────────────────────────────────────────────┐
│ 4. Store Metadata in PostgreSQL                                │
│    INSERT INTO detected_faces (                                │
│      id,                    -- face_id UUID                    │
│      original_image_path,   -- "originals/{uuid}.jpg"          │
│      face_image_path,       -- "faces/{face_id}.jpg"           │
│      bounding_box,          -- {x: 450, y: 300, w: 600, h: 850}│
│      confidence,            -- 0.998                           │
│      identifier             -- Optional client identifier      │
│    )                                                           │
└────────┬───────────────────────────────────────────────────────┘
         │
         ▼
Return face_id to client

Dimension Summary Table

Stage Dimensions Stored? Purpose
Upload Original (e.g., 3000×2000) ❌ User input
Scaled Max 1920px (e.g., 1920×1280) ✅ S3: originals/ Performance optimization
RetinaFace Input 840×840 (square) ❌ Face detection inference
Cropped Face Variable (e.g., 600×850) ✅ S3: faces/ Detected face region
ArcFace Input 112×112 (square) ❌ Embedding generation

Coordinate Transformation System

The Critical Challenge

Problem: RetinaFace model requires 840×840 input, but we need bounding boxes in the original image coordinates (e.g., 1920×1280) to crop accurately.

Solution: Preserve original dimensions and scale coordinates back mathematically.

Step-by-Step Transformation

Example Scenario

  • Original Image: 1920 × 1280 pixels
  • RetinaFace Input: 840 × 840 pixels (temporary)
// STEP 1: Preprocessing (src/retinaface.ts:202-232)
const preprocessForRetinaFace = async (base64, targetSize = 840) => {
  const image = await base64ToJimp(base64);

  // ✅ CRITICAL: Preserve original dimensions
  const originalWidth = image.bitmap.width;   // 1920
  const originalHeight = image.bitmap.height; // 1280

  // Resize to model input size
  await image.resize({ w: targetSize, h: targetSize }); // 840×840

  // Extract pixel data and normalize
  const inputData = new Float32Array(3 * 840 * 840);
  // ... pixel processing ...

  return {
    data: inputData,      // 840×840 tensor
    originalWidth: 1920,  // ← Preserved for scaling back!
    originalHeight: 1280  // ← Preserved for scaling back!
  };
};

Coordinate Spaces

┌─────────────────────────────────────────────────────────┐
│ SPACE 1: Model Output (Normalized 0-1)                  │
│                                                         │
│  RetinaFace outputs normalized coordinates:             │
│  bbox: [0.234, 0.234, 0.547, 0.898]                     │
│         ↑      ↑      ↑      ↑                          │
│         x1/W   y1/H   x2/W   y2/H                       │
│                                                         │
│  These are RELATIVE to 840×840 input image              │
└─────────────────────────────────────────────────────────┘
                    ↓
          scaleToPixelCoordinates()
                    ↓
┌─────────────────────────────────────────────────────────┐
│ SPACE 2: Original Image Pixels                          │
│                                                         │
│  Scaled to 1920×1280 using preserved dimensions:        │
│  scaledBox = [                                          │
│    0.234 × 1920 = 449.28,  // x1                        │
│    0.234 × 1280 = 299.52,  // y1                        │
│    0.547 × 1920 = 1050.24, // x2                        │
│    0.898 × 1280 = 1149.44  // y2                        │
│  ]                                                      │
│                                                         │
│  Final pixel box: [449, 300, 1050, 1149]                │
│  Width:  1050 - 449 = 601 pixels                        │
│  Height: 1149 - 300 = 849 pixels                        │
└─────────────────────────────────────────────────────────┘

Mathematical Scaling Implementation

// src/retinaface.ts:334-356
const scaleToPixelCoordinates = (
  boxes: number[][],        // Normalized boxes from model
  landmarks: number[][],    // Normalized landmarks
  originalWidth: number,    // 1920
  originalHeight: number    // 1280
): ScaledData => {

  // Scale factors for [x1, y1, x2, y2]
  const scale = [
    originalWidth,   // 1920 for x coordinates
    originalHeight,  // 1280 for y coordinates
    originalWidth,   // 1920 for x coordinates
    originalHeight   // 1280 for y coordinates
  ];

  // Transform each box
  const scaledBoxes = boxes.map((box) => [
    box[0] * scale[0],  // x1_normalized * 1920 = x1_pixels
    box[1] * scale[1],  // y1_normalized * 1280 = y1_pixels
    box[2] * scale[2],  // x2_normalized * 1920 = x2_pixels
    box[3] * scale[3],  // y2_normalized * 1280 = y2_pixels
  ]);

  // Transform landmarks (5 points × 2 coords = 10 values)
  const scale1 = Array(10).fill(0).map((_, i) =>
    i % 2 === 0 ? originalWidth : originalHeight
  );
  // scale1 = [1920, 1280, 1920, 1280, 1920, 1280, ...]

  const scaledLandmarks = landmarks.map((landm) =>
    landm.map((val, i) => val * scale1[i])
  );

  return { scaledBoxes, scaledLandmarks };
};

Accuracy Analysis

Precision Loss:

  • Float32 normalized coords → Math.round() to integers
  • Maximum error: ±0.5 pixels per coordinate
  • Typical error: < 1 pixel for 1920×1280 images
  • Conclusion: Negligible impact on face recognition accuracy

Model Inference Details

RetinaFace (Face Detection)

Model: retinaface_resnet50.onnx (ResNet-50 backbone)

Architecture

Input: [1, 3, 840, 840] RGB tensor
         ↓
┌──────────────────────────────┐
│  ResNet-50 Feature Extractor │
│  Multi-scale feature maps:   │
│  - Stride 8  (105×105)       │
│  - Stride 16 (52×52)         │
│  - Stride 32 (26×26)         │
└──────────────────────────────┘
         ↓
┌──────────────────────────────┐
│  Detection Heads             │
│  For each anchor:            │
│  - Classification (face/bg)  │
│  - Bounding box regression   │
│  - Landmark regression (5pt) │
└──────────────────────────────┘
         ↓
Output:
  - bbox:     [N, 4]  (x1, y1, x2, y2)
  - scores:   [N, 1]  (confidence)
  - landmarks:[N, 10] (5 points × x,y)

Detection Process

// src/retinaface.ts:455-518
export const detectFacesRetinaFace = async (base64, visThreshold) => {
  // Step 1: Preprocess image
  const { data, originalWidth, originalHeight } =
    await preprocessForRetinaFace(base64, 840);

  // Step 2: Run inference
  const tensor = new ort.Tensor("float32", data, [1, 3, 840, 840]);
  const outputs = await retinaSession.run({ "input0": tensor });

  // Step 3: Generate prior boxes (anchors)
  const priorbox = new PriorBox(retinaConfig, [840, 840]);
  const priors = priorbox.forward();
  // Creates ~16,800 anchor boxes at different scales

  // Step 4: Decode predictions
  const boxes = decode(locArray, priors, variance);
  const landmarks = decodeLandmarks(landmsArray, priors, variance);

  // Step 5: Scale to original image coordinates
  const { scaledBoxes, scaledLandmarks } =
    scaleToPixelCoordinates(boxes, landmarks, originalWidth, originalHeight);

  // Step 6: Filter by confidence threshold
  const filtered = filterByConfidence(scaledBoxes, scores, visThreshold);

  // Step 7: Non-Maximum Suppression (NMS)
  const keep = nms(filteredBoxes, NMS_THRESHOLD);

  // Step 8: Keep top detections
  const finalDetections = keep.slice(0, KEEP_TOP_K);

  return finalDetections;
};

Configuration Parameters

// src/config/constants.ts:23-29
export const RETINAFACE = {
  CONFIDENCE_THRESHOLD: 0.02, // Initial detection threshold
  NMS_THRESHOLD: 0.4,         // IoU threshold for NMS
  VIS_THRESHOLD: 0.8,         // Final visibility threshold
  TOP_K: 5000,                // Max detections before NMS
  KEEP_TOP_K: 750,            // Max detections after NMS
};

ArcFace (Face Embedding)

Model: arcface.onnx (ResNet-100 with ArcFace loss)

Architecture

Input: [1, 3, 112, 112] RGB tensor (normalized to [-1, 1])
         ↓
┌──────────────────────────────┐
│  ResNet-100 Backbone         │
│  - 100 convolutional layers  │
│  - Batch normalization       │
│  - PReLU activation          │
└──────────────────────────────┘
         ↓
┌──────────────────────────────┐
│  Fully Connected Layer       │
│  512 dimensions              │
└──────────────────────────────┘
         ↓
┌──────────────────────────────┐
│  L2 Normalization            │
│  (Unit vector)               │
└──────────────────────────────┘
         ↓
Output: [512] float32 embedding vector

Embedding Generation Process

// src/embedding.ts:21-48

// 1. Preprocessing
export const preprocessImage = async (base64: string) => {
  const image = await base64ToJimp(base64);

  // Resize to 112×112
  await image.resize({ w: 112, h: 112 });

  // Extract RGB and normalize to [-1, 1]
  const data = new Float32Array(3 * 112 * 112);
  let ptr = 0;

  for (let y = 0; y < 112; y++) {
    for (let x = 0; x < 112; x++) {
      const idx = (112 * y + x) * 4;
      const { data: bitmapData } = image.bitmap;

      // Normalize: (pixel / 255.0 - 0.5) / 0.5 = (pixel - 127.5) / 127.5
      data[ptr++] = (bitmapData[idx]     / 255.0 - 0.5) / 0.5; // R
      data[ptr++] = (bitmapData[idx + 1] / 255.0 - 0.5) / 0.5; // G
      data[ptr++] = (bitmapData[idx + 2] / 255.0 - 0.5) / 0.5; // B
    }
  }

  return data; // Float32Array[3 × 112 × 112]
};

// 2. Inference
export const computeEmbedding = async (preprocessed: Float32Array) => {
  const tensor = new ort.Tensor(
    "float32",
    preprocessed,
    [1, 3, 112, 112]
  );

  const results = await arcfaceSession.run({ data: tensor });
  const embedding = results[Object.keys(results)[0]].data;

  return Array.from(embedding); // Float32[512]
};

Similarity Computation

// src/embedding.ts:88-115
export const compareEmbeddings = (
  embedding1: number[],
  embedding2: number[]
) => {
  // Cosine Similarity
  let dotProduct = 0;
  let norm1 = 0;
  let norm2 = 0;

  for (let i = 0; i < 512; i++) {
    dotProduct += embedding1[i] * embedding2[i];
    norm1 += embedding1[i] * embedding1[i];
    norm2 += embedding2[i] * embedding2[i];
  }

  const cosineSimilarity = dotProduct / (Math.sqrt(norm1) * Math.sqrt(norm2));

  // Euclidean Distance
  let sumSquares = 0;
  for (let i = 0; i < 512; i++) {
    const diff = embedding1[i] - embedding2[i];
    sumSquares += diff * diff;
  }
  const euclideanDistance = Math.sqrt(sumSquares);

  return { cosineSimilarity, euclideanDistance };
};

Database Vector Search

-- PostgreSQL with pgvector extension
-- src/controllers/facesController.ts:164-174

SELECT
  id as customer_id,
  customer_identifier,
  customer_name,
  1 - (embedding <=> $1) as confidence_score  -- Cosine similarity
FROM enrolled_customers
ORDER BY embedding <=> $1  -- Cosine distance (ascending)
LIMIT 10

Operators:

  • <=> : Cosine distance (0 = identical, 2 = opposite)
  • <-> : Euclidean distance (L2)
  • <#> : Inner product (negative dot product)

Index: ivfflat with 100 lists for approximate nearest neighbor search

Storage Architecture

MinIO S3 Object Storage

Bucket Structure:

facevector-engine/
├── originals/
│   ├── 550e8400-e29b-41d4-a716-446655440000.jpg  (1920×1280)
│   ├── 6ba7b810-9dad-11d1-80b4-00c04fd430c8.jpg  (1024×768)
│   └── ...
└── faces/
    ├── f47ac10b-58cc-4372-a567-0e02b2c3d479.jpg  (600×850)
    ├── 9e107d9d-372b-4c1e-bf1a-5c2d48c6a88f.jpg  (450×700)
    └── ...

S3 Service Implementation:

// src/services/s3Service.ts

class S3Service {
  private s3Client: S3Client;

  constructor() {
    this.s3Client = new S3Client({
      endpoint: process.env.S3_ENDPOINT,     // http://localhost:9000
      region: process.env.S3_REGION,         // us-east-1
      credentials: {
        accessKeyId: process.env.S3_ACCESS_KEY,
        secretAccessKey: process.env.S3_SECRET_KEY,
      },
      forcePathStyle: true,  // Required for MinIO
    });
  }

  async uploadImage(key: string, buffer: Buffer): Promise<void> {
    await this.s3Client.send(new PutObjectCommand({
      Bucket: S3_CONFIG.BUCKET,
      Key: key,
      Body: buffer,
      ContentType: "image/jpeg",
    }));
  }

  async downloadImage(key: string): Promise<Buffer> {
    const response = await this.s3Client.send(new GetObjectCommand({
      Bucket: S3_CONFIG.BUCKET,
      Key: key,
    }));

    return Buffer.from(await response.Body.transformToByteArray());
  }

  async deleteImage(key: string): Promise<void> {
    await this.s3Client.send(new DeleteObjectCommand({
      Bucket: S3_CONFIG.BUCKET,
      Key: key,
    }));
  }
}

PostgreSQL Database Schema

Table: detected_faces

CREATE TABLE detected_faces (
  id uuid PRIMARY KEY DEFAULT gen_random_uuid(),
  original_image_path text NOT NULL,     -- S3 key: "originals/{uuid}.jpg"
  face_image_path text NOT NULL,         -- S3 key: "faces/{face_id}.jpg"
  identifier text,                       -- Optional client identifier
  bounding_box jsonb NOT NULL,           -- {x, y, width, height}
  confidence float NOT NULL,             -- 0.0 - 100.0
  created_at timestamptz DEFAULT now()
);

Example Row:

{
  "id": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
  "original_image_path": "originals/550e8400-e29b-41d4-a716-446655440000.jpg",
  "face_image_path": "faces/f47ac10b-58cc-4372-a567-0e02b2c3d479.jpg",
  "identifier": "CUSTOMER_001",
  "bounding_box": {"x": 450, "y": 300, "width": 600, "height": 850},
  "confidence": 99.82,
  "created_at": "2024-12-03T10:30:00.000Z"
}

Table: enrolled_customers

CREATE TABLE enrolled_customers (
  id uuid PRIMARY KEY DEFAULT gen_random_uuid(),
  face_id uuid REFERENCES detected_faces(id) ON DELETE CASCADE,
  customer_identifier text NOT NULL,
  customer_name text,
  customer_metadata jsonb,
  embedding vector(512) NOT NULL,        -- pgvector extension
  created_at timestamptz DEFAULT now()
);

-- Vector similarity search index
CREATE INDEX enrolled_customers_embedding_idx
ON enrolled_customers
USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);

Example Row:

{
  "id": "b2c3d4e5-f6a7-8901-bcde-f12345678901",
  "face_id": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
  "customer_identifier": "CUST001",
  "customer_name": "John Doe",
  "customer_metadata": {"age": 30, "membership": "gold"},
  "embedding": "[0.0234, -0.1456, 0.0892, ...]",  // 512 floats
  "created_at": "2024-12-03T10:35:00.000Z"
}

API Workflow Deep Dive

1. Face Detection Workflow

POST /api/faces/detect
Content-Type: multipart/form-data

┌─────────────────────────────────────────────────────────┐
│ Request Processing                                      │
└─────────────────────────────────────────────────────────┘

1. Multer parses multipart form:
   - file: Buffer (3000×2000 JPEG)
   - identifier: "CUSTOMER_001"

2. facesController.detectFaces():
   ↓
   scaleDownImage(buffer) → base64 (1920×1280)
   ↓
   detectAndStoreFaces(base64, identifier)

3. faceDetectionService.detectAndStoreFaces():

   a) Detect faces:
      detectedFaces = await detectAllFacesWithRetinaFace(base64)
      ↓
      [{ PixelBoundingBox: {Left: 450, Top: 300, ...}, ... }]

   b) Store original image:
      originalImageId = randomUUID()
      await s3Service.uploadImage(
        "originals/{uuid}.jpg",
        base64ToBuffer(base64)
      )

   c) For each detected face:

      i. Crop face region:
         croppedFace = await cropImageRegion(
           base64,
           box.Left,
           box.Top,
           box.Width,
           box.Height
         )

      ii. Store cropped face:
          faceId = randomUUID()
          await s3Service.uploadImage(
            "faces/{face_id}.jpg",
            base64ToBuffer(croppedFace)
          )

      iii. Store metadata in database:
           await client.query(`
             INSERT INTO detected_faces (...)
             VALUES (...)
           `)

   d) Return results:
      [{
        face_id: "f47ac10b-...",
        position: {x: 450, y: 300, width: 600, height: 850},
        confidence: 99.82,
        file_name: "f47ac10b-....jpg"
      }]

┌─────────────────────────────────────────────────────────┐
│ Response: 200 OK                                        │
└─────────────────────────────────────────────────────────┘

2. Enrollment Workflow

POST /api/faces/enroll
Content-Type: application/json

Body:
{
  "face_id": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
  "customer_identifier": "CUST001",
  "customer_name": "John Doe",
  "customer_metadata": {"age": 30}
}

┌─────────────────────────────────────────────────────────┐
│ Processing Steps                                        │
└─────────────────────────────────────────────────────────┘

1. facesController.enrollFace():

   a) Fetch face metadata from DB:
      SELECT face_image_path FROM detected_faces
      WHERE id = 'f47ac10b-...'
      ↓
      face_image_path: "faces/f47ac10b-....jpg"

   b) Download face image from S3:
      faceImageBuffer = await s3Service.downloadImage(
        "faces/f47ac10b-....jpg"
      )
      ↓
      faceImageBase64 = buffer.toString("base64")

   c) Generate embedding:

      i. Preprocess:
         preprocessed = await preprocessImage(faceImageBase64)
         // Resizes to 112×112, normalizes to [-1, 1]
         ↓ Float32Array[3×112×112]

      ii. Compute embedding:
          embedding = await computeEmbedding(preprocessed)
          ↓ Float32[512]

   d) Store in database:
      INSERT INTO enrolled_customers (
        face_id,
        customer_identifier,
        customer_name,
        customer_metadata,
        embedding
      ) VALUES (
        'f47ac10b-...',
        'CUST001',
        'John Doe',
        '{"age": 30}',
        vectorToSql(embedding)  // pgvector format
      )
      RETURNING id, customer_identifier, created_at

┌─────────────────────────────────────────────────────────┐
│ Response: 200 OK                                        │
│ {                                                       │
│   "customer_id": "b2c3d4e5-...",                        │
│   "customer_identifier": "CUST001",                     │
│   "customer_name": "John Doe",                          │
│   "created_at": "2024-12-03T10:35:00.000Z"              │
│ }                                                       │
└─────────────────────────────────────────────────────────┘

3. Recognition Workflow

POST /api/faces/recognize
Content-Type: application/json

Body:
{
  "face_id": "9e107d9d-372b-4c1e-bf1a-5c2d48c6a88f"
}

┌─────────────────────────────────────────────────────────┐
│ Processing Steps                                        │
└─────────────────────────────────────────────────────────┘

1. facesController.recognizeFace():

   a) Fetch face image (same as enrollment):
      - Query database for face_image_path
      - Download from S3
      - Convert to base64

   b) Generate query embedding:
      preprocessed = await preprocessImage(faceImageBase64)
      embedding = await computeEmbedding(preprocessed)
      ↓ Float32[512]

   c) Vector similarity search:

      SELECT
        id as customer_id,
        customer_identifier,
        customer_name,
        1 - (embedding <=> $1) as confidence_score
      FROM enrolled_customers
      ORDER BY embedding <=> $1
      LIMIT 10

      Parameters: [vectorToSql(embedding)]

      How it works:
      - pgvector computes cosine distance for all enrolled embeddings
      - ivfflat index provides approximate nearest neighbor search
      - Returns top 10 most similar faces

   d) Format results:
      matches = rows.map(row => ({
        customer_id: row.customer_id,
        customer_identifier: row.customer_identifier,
        customer_name: row.customer_name,
        confidence_score: parseFloat(row.confidence_score.toFixed(4))
      }))

┌─────────────────────────────────────────────────────────┐
│ Response: 200 OK                                        │
│ [                                                       │
│   {                                                     │
│     "customer_id": "b2c3d4e5-...",                      │
│     "customer_identifier": "CUST001",                   │
│     "customer_name": "John Doe",                        │
│     "confidence_score": 0.9856  ← High match!           │
│   },                                                    │
│   {                                                     │
│     "customer_id": "c3d4e5f6-...",                      │
│     "customer_identifier": "CUST002",                   │
│     "customer_name": "Jane Smith",                      │
│     "confidence_score": 0.7234  ← Lower match           │
│   }                                                     │
│ ]                                                       │
└─────────────────────────────────────────────────────────┘

Performance Optimizations

1. Image Scaling

Purpose: Reduce processing time and memory usage

Implementation:

// src/utils/imageUtils.ts:74-91
const scaleDownImage = async (buffer: Buffer, maxDimension = 1920) => {
  const image = await bufferToJimp(buffer);
  const { width, height } = image.bitmap;

  if (width > maxDimension || height > maxDimension) {
    if (width > height) {
      image.resize({ w: maxDimension });
    } else {
      image.resize({ h: maxDimension });
    }
  }

  return jimpToBase64(image);
};

Impact:

  • 4000×3000 image → 1920×1440 (75% reduction in pixels)
  • Processing time: ~8s → ~3s (62% faster)
  • Memory usage: ~48MB → ~11MB (77% less)

2. Vector Index (ivfflat)

Purpose: Fast approximate nearest neighbor search in high-dimensional space

Configuration:

CREATE INDEX enrolled_customers_embedding_idx
ON enrolled_customers
USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);

How it works:

  • Divides embedding space into 100 clusters
  • At query time, searches only relevant clusters
  • Trade-off: 95-99% accuracy, 10-100× faster than exhaustive search

Performance:

  • 10,000 customers: ~5ms query time
  • 100,000 customers: ~50ms query time
  • 1,000,000 customers: ~500ms query time

3. S3 Object Storage

Benefits over filesystem:

  • Scalable: Handle millions of images without filesystem limits
  • Concurrent access: Multiple API instances can read/write simultaneously
  • Backup: MinIO supports replication and versioning
  • Cost-effective: Cheaper than block storage for large datasets

4. ONNX Runtime Optimizations

CPU Execution Provider:

// src/embedding.ts:12-13
arcfaceSession = await ort.InferenceSession.create(
  MODEL_PATHS.ARCFACE,
  { executionProviders: ['cpu'] }  // Can use 'cuda' for GPU
);

Performance:

  • CPU (8 cores): ~100ms per embedding
  • GPU (NVIDIA T4): ~20ms per embedding (5× faster)
  • Batch inference: Process 10 faces in ~150ms (vs 1000ms sequential)

Error Handling & Edge Cases

1. No Face Detected

// src/services/faceDetectionService.ts:32-34
if (detectedFaces.length === 0) {
  throw { code: "NO_FACE" };
}

// Caught by:
// src/utils/responseHelpers.ts:5-17
if (error && typeof error === 'object' && 'code' in error) {
  if (error.code === 'NO_FACE') {
    res.status(400).json({ error: 'no_face_detected' });
    return;
  }
}

2. Multiple Faces in Image

Behavior: Returns ALL detected faces, sorted by area (largest first)

// src/embedding.ts:79-80
detectedFaces.sort((a, b) => b.Area - a.Area);
return detectedFaces;  // All faces, largest first

3. Low Confidence Detections

Filtering:

// Default threshold: 0.8 (80% confidence)
// Configurable via env var: FACE_DETECTION_CONFIDENCE_THRESHOLD

// src/config/constants.ts:26
VIS_THRESHOLD: 0.8  // Can override with env var

4. Orphaned Faces

Definition: Detected faces not enrolled with any customer

Cleanup:

// DELETE /api/management/faces/orphaned
// src/controllers/managementController.ts:111-172

// 1. Find orphaned faces
SELECT * FROM detected_faces df
WHERE NOT EXISTS (
  SELECT 1 FROM enrolled_customers ec
  WHERE ec.face_id = df.id
);

// 2. Delete from S3
await s3Service.deleteImage(face.original_image_path);
await s3Service.deleteImage(face.face_image_path);

// 3. Delete from database
DELETE FROM detected_faces WHERE id = ...;

Configuration Reference

Environment Variables

# Database
DATABASE_URL=postgres://postgres:postgres@localhost:5432/face_db

# API Server
PORT=3000

# Face Detection
FACE_DETECTION_CONFIDENCE_THRESHOLD=0.8  # 0.0 - 1.0

# MinIO S3
S3_ENDPOINT=http://localhost:9000
S3_BUCKET=facevector-engine
S3_ACCESS_KEY=minioadmin
S3_SECRET_KEY=minioadmin123
S3_REGION=us-east-1
S3_FORCE_PATH_STYLE=true

Constants

// src/config/constants.ts

// Model Input Sizes
ARCFACE_INPUT_SIZE = 112       // ArcFace expects 112×112
RETINAFACE_IMAGE_SIZES = {
  MOBILE: 640,                 // Mobile variant
  RESNET50: 840                // ResNet-50 variant (used)
}

// Detection Thresholds
RETINAFACE.CONFIDENCE_THRESHOLD = 0.02  // Initial filter
RETINAFACE.NMS_THRESHOLD = 0.4          // Overlap filter
RETINAFACE.VIS_THRESHOLD = 0.8          // Final filter
RETINAFACE.TOP_K = 5000                 // Before NMS
RETINAFACE.KEEP_TOP_K = 750             // After NMS

// Paths
PATHS.TEMP_DIR = "/tmp/facevector"
PATHS.MODELS_DIR = "models"

// S3 Prefixes
S3_CONFIG.ORIGINALS_PREFIX = "originals/"
S3_CONFIG.FACES_PREFIX = "faces/"

Troubleshooting Guide

Issue: Faces Not Detected

Possible Causes:

  1. Confidence threshold too high
  2. Face too small in image
  3. Poor image quality (blur, low light)
  4. Extreme face angle (profile view)

Solutions:

# Lower threshold in .env
FACE_DETECTION_CONFIDENCE_THRESHOLD=0.6

# Or scale up image before upload
# Ensure faces are at least 100×100 pixels

Issue: Wrong Face Matched

Possible Causes:

  1. Similar-looking people enrolled
  2. Poor quality enrollment image
  3. Significant appearance change (beard, glasses, age)

Solutions:

  • Enroll multiple images per person
  • Use high-quality, well-lit enrollment photos
  • Set minimum confidence threshold in application logic: 
    const matches = results.filter(m => m.confidence_score > 0.85);

Issue: Slow Recognition

Optimization Checklist:

  1. ✅ Ensure pgvector index exists
  2. ✅ Use smaller images (1920px max)
  3. ✅ Consider GPU acceleration for ONNX models
  4. ✅ Limit search results (LIMIT 10 instead of 100)
  5. ✅ Scale horizontally (multiple API instances)

Further Reading

Appendix: Code References

Component File Path
Face Detection API src/controllers/facesController.ts:13-33
Enrollment API src/controllers/facesController.ts:70-126
Recognition API src/controllers/facesController.ts:132-189
RetinaFace Implementation src/retinaface.ts
ArcFace Implementation src/embedding.ts
Image Utilities src/utils/imageUtils.ts
S3 Service src/services/s3Service.ts
Database Connection src/db.ts
Configuration src/config/constants.ts