TinEye was one of the first reverse image search engines on the web, launching in 2008 with a focus on finding exact and near-exact image matches. Over the years, it has built a substantial index of web images. facesearching, while newer, has taken a different approach — focusing specifically on face recognition and indexing sources that matter for identity verification. This comparison examines the database size, coverage, and source diversity of both platforms, helping you understand which face search engine is more likely to find the person you are looking for.
Database Size: Quantity vs. Relevance
TinEye: A Large General Image Index
TinEye has indexed over 60 billion images since its launch, making it one of the largest reverse image search databases in existence. However, TinEye's index is built for general image matching — it catalogs product photos, stock images, memes, logos, and every other type of image found on the web. While this breadth is impressive, it means that a significant portion of TinEye's database is not relevant to face search. When you search for a face, TinEye is searching through billions of non-face images, which can dilute the quality and relevance of results.
facesearching: A Curated Face-Focused Index
facesearching takes a curated approach. Rather than indexing every image on the web, facesearching focuses on sources where faces are likely to appear with identity-related context — social media profiles, news articles, professional directories, blogs, and public databases. This targeted indexing means that while facesearching's total image count may be smaller than TinEye's, the relevance of its index for face search is significantly higher. Every image in facesearching's database is indexed because it contains a face that could be relevant to identity verification.
Source Coverage: Where the Images Come From
facesearching Source Coverage
- Social media platforms: 100+ social networks including Instagram, Facebook, Twitter/X, LinkedIn, TikTok, VK, and regional platforms across Asia, Europe, and the Americas.
- News sites: Major news outlets, local news publications, and press release databases where faces appear in articles and reports.
- Professional directories: LinkedIn, company websites, industry association pages, and professional profiles.
- Blogs and personal websites: Personal blogs, portfolio sites, and other self-published content where individuals post their own photos.
- Public databases: Public records, academic publications, conference websites, and other publicly accessible databases.
- Video platforms: Thumbnails and frames from YouTube, Vimeo, and other video hosting platforms.
TinEye Source Coverage
- General web images: A broad crawl of the public web, including product images, stock photos, illustrations, and all other image types.
- News and media: Images from news websites and media publications.
- E-commerce: Product images from online stores and marketplaces.
- Stock photography: Images from stock photo sites and creative commons repositories.
- Social media: Limited social media coverage compared to facesearching, as many social platforms restrict crawling.
Social Media Coverage: The Critical Difference
The most important difference between facesearching and TinEye for face search is social media coverage. Most people who want to find someone by photo are looking for social media profiles — the Instagram account of a potential date, the Facebook profile of an online seller, or the LinkedIn page of a job candidate. TinEye's general-purpose crawling approach has limited coverage of social media platforms because many platforms restrict or block general crawlers. facesearching has invested specifically in social media coverage, indexing profiles across 100+ platforms to provide the most relevant results for identity verification. For investigative use cases, see how reverse face search is transforming OSINT investigations.
Update Frequency and Freshness
facesearching maintains a high-frequency update cycle, continuously indexing new content to ensure that recently posted photos appear in search results. This is particularly important for identity verification because scammers often create new fake profiles with recently stolen photos. TinEye's update frequency varies by source, and some parts of its index may be refreshed less frequently. For time-sensitive searches — like verifying a new online connection — a fresher index is a significant advantage. For a broader comparison of the two platforms, see our full facesearching vs TinEye comparison.
A database of 60 billion images is impressive, but if 59 billion of them are product photos and memes, it does not help you find someone by photo. facesearching's curated face-focused index is built for the task you actually need: verifying identities.