Advanced Facial Comparison Software — User Guide

[IMPORTANT NOT ACCURATE YET — GETTING THERE]

I revised this software entirely to avoid the earlier issues: https://6mzld2.csb.app/

User Guide: Face Detection and Comparison Software

Overview

This software utilizes advanced facial recognition techniques to detect and compare faces in images. It employs machine learning models that analyze facial landmarks and descriptors to determine the similarity between two faces. The goal is to provide insights into whether two images depict the same person or if they are doppelgängers (look-alikes).

Key Features

1. Face Detection: Automatically identifies and locates faces in uploaded images.
2. Landmark Detection: Analyzes specific points on the face to understand its geometry.
3. Descriptor Calculation: Generates unique numerical representations (embeddings) of faces.
4. Similarity Comparison: Compares faces based on landmarks and descriptors.
5. Doppelgänger Detection: Suggests potential look-alikes based on the comparison results.

How to Use the Software

1. Upload Images

• Click on the “Choose File” button under the First Image and Second Image sections to upload images from your device.
• The software supports common image formats (e.g., JPEG, PNG).

2. Image Processing

• Once an image is uploaded, the software automatically detects faces in the image.
• The detected face will be highlighted, and landmark points will be drawn on the image.

3. Compare Faces

• After both images have been processed, click the “Compare Faces” button.
• The software will evaluate the similarity between the two detected faces.

4. View Results

• The results will display the overall similarity score, descriptor similarity, landmark similarity, and whether the faces match.
• If the conditions suggest that the images may depict doppelgängers, a warning message will appear.

5. Download Screenshot

• After comparison, you can download a screenshot of the results by clicking the “Download Screenshot” button.

Theory Behind Measurements

1. Face Detection

• Theory: The software uses a convolutional neural network (CNN) to detect faces in images. This model is trained on thousands of images to recognize patterns associated with human faces.
• Process: The detection process identifies the bounding box around the face and returns the coordinates of this region.

2. Landmark Detection

• Theory: Facial landmarks are specific points on a face that correspond to key features (e.g., eyes, nose, mouth). Landmark detection relies on predefined models that understand the typical structure of human faces.
• Process: The software detects 68 landmark points on the face, which helps to analyze its geometry. These points are used to assess the spatial arrangement and proportions of facial features.

3. Descriptor Calculation

• Theory: A face descriptor is a high-dimensional vector that encodes distinctive features of the face, capturing its unique characteristics. This vector is generated using deep learning models trained on facial images.
• Process: The software extracts face embeddings from the detected landmarks and converts them into a descriptor vector. This vector can represent the face in a more abstract space, allowing for easier comparison.

4. Similarity Comparison

• Theory: The similarity between two faces is evaluated using both landmark and descriptor comparisons. The combination of these two measurements provides a comprehensive analysis of how closely related the faces are.
• Process:
• Landmark Similarity: Uses Procrustes analysis to compare the normalized landmark positions of the two faces. A higher score indicates that the facial structures are similar.
• Descriptor Similarity: Uses the Euclidean distance between the descriptor vectors of the two faces. A smaller distance indicates greater similarity.

5. Doppelgänger Detection

• Theory: A high landmark similarity combined with low descriptor similarity can indicate that two faces look alike (doppelgängers) but are different individuals. This phenomenon occurs because the geometric structure of the face may be similar, while textural details differ.
• Process: The software checks if the landmark similarity is above a specified threshold (e.g., 50%) and if the descriptor similarity is below another threshold (e.g., 30%). If both conditions are met, it suggests the possibility of a doppelgänger.

Understanding the Results

• Overall Similarity Score: A composite score combining both landmark and descriptor similarities.
• Descriptor Similarity: Indicates how similar the finer details of the faces are.
• Landmark Similarity: Reflects how closely the geometric structure of the faces matches.
• Match Status: Displays whether the faces are a likely match based on the overall similarity score and the set threshold.
• Doppelgänger Warning: Alerts the user when there is high landmark similarity but low descriptor similarity, indicating the possibility of look-alike individuals.

Older Version.

New Example

Old link to previous DEMO software:

https://c8nnv4.csb.app/

Note: you may experience issues (like having to press compare twice) but over time I will paste the link to any revised versions here. The sandbox can be used to copy the code.Introduction

The Advanced Facial Comparison Software is a sophisticated tool that uses AI-powered face recognition to compare two facial images and determine their similarity. It employs multiple comparison methods and customizable settings for optimal results.

Getting Started

System Requirements

• Modern web browser (Chrome, Firefox, Safari, Edge)
• Stable internet connection
• Minimum 4GB RAM recommended

Initial Setup

1. Launch the application
2. Wait for the “Loading Face Recognition Models” message to complete
3. The interface will become active when ready to use

Features

• Real-time face comparison
• Multiple comparison methods
• Adjustable settings
• Processing status indicator
• Percentage-based similarity scoring
• Image preprocessing capabilities

Using the Software

Basic Operation

1. Upload Images

• Click “Choose File” under “First Image”
• Select a clear photo containing a face
• Repeat for “Second Image”
• Supported formats: JPG, PNG, JPEG

2. Compare Images

• Click the “Compare Faces” button
• Wait for processing (spinner ‘may’ appear). NOTE It may be necessary to press a continue button as delays seem to occur in this demo software via sandbox.
• View results showing similarity percentage

Understanding Results

Similarity Score: 0–100%

• Green result (>60%): High probability of same person
• Red result (<60%): Low probability of same person

Advanced Settings

Comparison Methods

1. Cosine Similarity

• Best for general comparisons
• More tolerant of lighting variations
• Cosine Similarity: Measures the angle between vectors, good for comparing facial features regardless of intensity

2. Euclidean Distance

• More strict comparison
• Better for controlled environments
• Euclidean Distance: Measures actual distance between points, more sensitive to absolute differences

3. Weighted Comparison [dropped from this version]

• Emphasizes specific facial features
• Best for detailed analysis

Adjustable Parameters

Similarity Threshold

• Range: 0.0–1.0
• Default: 0.5
• Higher values = stricter matching
• Lower values = more lenient matching
• Sets the cutoff point for determining if faces match
• Higher threshold (e.g., 0.75) = stricter matching
• Lower threshold (e.g., 0.25) = more lenient matching

Image Preprocessing

1. Smoothing

• Range: 0.0–1.0
• Helpful for lower quality images
• Applies blur to reduce noise
• Higher values reduce detail but can help with noisy images
• 0 means no smoothing applied

2. Normalization

• function removed.

3. Histogram Equalization

• Checkbox option
• Improves contrast
• Useful for poor lighting conditions
• When enabled, normalizes image contrast
• Can help with poorly lit images
• “Currently placeholder in the code”

Troubleshooting

Common Issues and Solutions

1. “No face detected” Error

• Ensure face is clearly visible
• Check image lighting
• Try different angle
• Reduce image size if >5MB

2. Low Accuracy

• Try different comparison methods
• Adjust threshold settings
• Enable preprocessing options
• Use clearer images

3. Slow Processing

• Check internet connection
• Reduce image size
• Close other browser tabs
• Refresh page if persistent

Best Practices

Image Guidelines

1. Quality

• Use clear, focused images
• Minimum resolution: 640x480
• Avoid blurry or pixelated images

2. Composition

• Face should be centered
• Neutral expression preferred
• Good lighting
• Minimal background clutter

3. Positioning

• Front-facing preferred
• Minimal head tilt
• Both eyes visible
• No obstructions (glasses, masks)

Optimal Settings

1. For Same-Day Photos

• Method: Cosine Similarity
• Threshold: 0.3
• Minimal preprocessing needed

2. For Different-Age Comparisons [dropped from this version]

• Method: Weighted Comparison
• Threshold: 0.2–0.3
• Enable normalization
• Enable histogram equalization

3. For Poor Quality Images

• Enable all preprocessing options
• Increase smoothing to 0.5–0.7
• Lower threshold to 0.2

Tips for Best Results

1. Use recent photos when possible
2. Maintain consistent lighting between images
3. Start with default settings, then adjust as needed
4. Multiple comparisons may be necessary for conclusive results

Need any specific section expanded or additional information added?