Top Free Face Compare tools, APIs, and Open Source models

What is Face Compare API?

The Face Compare API, also called Face Similarity API, is a tool that enables app or system developers to integrate facial recognition and comparison functions. This API evaluates the closeness or similarity of two facial photographs based on facial traits. By applying robust algorithms and machine learning techniques, the Face Compare API can identify the resemblances and distinctions between two faces by analysing facial landmarks, patterns and attributes.

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Here, you will compare two facial images to determine if they belong to the same person. The API requires two images: a reference image, such as the user's official ID, and a query image, like a selfie used for validation.

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Top Open Source (Free) Face Compare models on the market

For users seeking a cost-effective engine, opting for an open-source model is the recommended choice. Here is the list of the best Face Similarity Open Source Models:

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1‍. Exadel

Exadel CompreFace is a cost-free and open-source facial recognition service that can be seamlessly integrated into any system without requiring prior knowledge in machine learning. It can be easily deployed using docker.

CompreFace furnishes a REST API for facial recognition, verification, and detection, as well as landmark detection, mask detection, head pose detection, and age and gender identification.

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2. DeepFaceLab

DeepFaceLab is a widely used open-source platform for creating deepfakes. It implements deep learning techniques to manipulate and exchange facial features. Although it primarily concentrates on producing deepfakes, it inherently entails face matching as a component of the procedure.

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3. face-comparison

AI Face comparison using FaceNet, compare two photos and see if they are the same person.

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4. Dlib

Dlib is a C++ library that provides a range of machine-learning tools, including facial recognition. It features a pre-trained face detection model that is well-regarded for its robustness, operating effectively in various orientations and lighting conditions.

Dlib also has integrated Python bindings, allowing it to be used with a variety of programming languages. You can also use this for face compare.

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5. MTCNN

The MTCNN (Multi-Task Cascaded Convolutional Networks) algorithm has transformed the face detection and recognition industry since its development in 2016. The technology uses a series of cascading neural networks to precisely and quickly detect, align, and extract facial features from digital images. You can also use this for face compare.

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Cons of Using Open Source AI models

‍While open source models offer many advantages, they also come with some potential drawbacks and challenges. Here are some cons of using open source models:

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• Not Entirely Cost Free: Open-source models, while providing valuable resources to users, may not always be entirely free of cost. Users often need to bear expenses related to hosting and server usage, especially when dealing with large or resource-intensive data sets.
• Lack of Support: Open source models may not have official support channels or dedicated customer support teams. If you encounter issues or need assistance, you might have to rely on community forums or the goodwill of volunteers, which can be less reliable than commercial support.
• Limited Documentation: Some open source models may have incomplete or poorly maintained documentation. This can make it difficult for developers to understand how to use the model effectively, leading to frustration and wasted time.
• Security Concerns: Security vulnerabilities can exist in open source models, and it may take longer for these issues to be addressed compared to commercially supported models. Users of open source models may need to monitor for security updates and patches actively.
• Scalability and Performance: Open source models may not be as optimized for performance and scalability as commercial models. If your application requires high performance or needs to handle a large number of requests, you may need to invest more time in optimization.

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Why choose Eden AI?

Given the potential costs and challenges related to open-source models, one cost-effective solution is to use APIs. Eden AI smoothens the incorporation and implementation of AI technologies with its API, connecting to multiple AI engines.

Eden AI presents a broad range of AI APIs on its platform, customized to suit your specific needs and financial limitations. These technologies include data parsing, language identification, sentiment analysis, logo recognition, question answering, data anonymization, speech recognition, and numerous other capabilities.

To get started, we offer free $10 credits for you to explore our APIs.

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Access Face Compare providers with one API

Our standardized API enables you to integrate Face Compare APIs into your system with ease by utilizing various providers on Eden AI. Here is the list (in alphabetical order):

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• Amazon
• Base64
• Face++

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1. Amazon- Available on Eden AI

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Rekognition, an API for facial comparison, analysis and identification, is capable of integrating into a range of applications. With its face comparison feature, it can be used to determine the similarity or likeness between two face images. This can be applied for numerous purposes, including identity verification or identifying similar faces from a collection.

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2. Base64- Available on Eden AI

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Base64.ai's Face Compare API is a potent tool for facial recognition and comparison. It enables developers to incorporate sturdy face matching abilities into their applications seamlessly. Through advanced algorithms, it adeptly evaluates and contrasts faces, rendering it optimal for security, verification, and tailored user interactions. The API facilitates multiple image formats and offers a streamlined approach to refine facial recognition traits in diverse applications.

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3. Face++- Available on Eden AI

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Face++ offers a complete suite of facial recognition APIs, such as face comparison, detection and analysis, that are widely used throughout various industries. The tool is highly accurate and precise. The Face Comparison API allows developers to determine the similarity or likeness between two facial images based on facial features and characteristics.

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Pricing Structure for Face Similarity API Providers

Eden AI offers a user-friendly platform for evaluating pricing information from diverse API providers and monitoring price changes over time. As a result, keeping up-to-date with the latest pricing is crucial. The pricing chart below outlines the rates for smaller quantities for November 2023, as well as you can get discounts for potentially large volumes.

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How Eden AI can help you?

Eden AI is the future of AI usage in companies: our app allows you to call multiple AI APIs.

• Centralized and fully monitored billing on Eden AI for Face Compare APIs
• Unified API for all providers: simple and standard to use, quick switch between providers, access to the specific features of each provider
• Standardized response format: the JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
• The best Artificial Intelligence APIs in the market are available: big cloud providers (Google, AWS, Microsoft, and more specialized engines)
• Data protection: Eden AI will not store or use any data. Possibility to filter to use only GDPR engines.

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Next step in your project

The Eden AI team can help you with your Face Compare integration project. This can be done by :

• Organizing a product demo and a discussion to understand your needs better. You can book a time slot on this link: Contact
• By testing the public version of Eden AI for free: however, not all providers are available on this version. Some are only available on the Enterprise version.
• By benefiting from the support and advice of a team of experts to find the optimal combination of providers according to the specifics of your needs
• Having the possibility to integrate on a third-party platform: we can quickly develop connectors.

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