facesearching and Pipl are both people search tools, but they start from opposite ends of the problem. Pipl is a name-based people search engine that aggregates public records, contact information, and social profiles by querying identifiers like a name, email, username, or phone number. facesearching is a photo-based face search engine that identifies people using biometric facial recognition. The difference in starting point — a name versus a photo — shapes everything that follows, from accuracy to use cases to privacy. This comparison breaks down how each tool works and which one fits your situation. For the underlying technology, see our complete guide to reverse face search.
How Pipl Works
Pipl is a people search and identity resolution platform widely used by investigators, businesses, and journalists. Its strength is linking scattered data points — a name, an email address, a phone number, a username, a physical address — into a unified profile of a single individual. When you have a real name and one or two corroborating identifiers, Pipl can pull together a remarkably complete picture from public records, data broker aggregations, and indexed social profiles. The platform is designed for identity resolution at scale, which makes it powerful for due diligence, fraud prevention, and skip tracing. The core limitation is that Pipl requires you to already know who you are looking for. If you have only a photograph and no name, Pipl has no starting point, because its engine is keyed to text identifiers, not faces.
How facesearching Works
facesearching flips the equation. You do not need a name, an email, or any text identifier to begin. You upload a single photo, and the platform isolates the face, converts it into a biometric template, and matches it against a large index of faces extracted from publicly available web pages. The results surface social media profiles, news articles, blog posts, and video content where the same face appears — often revealing the person's name and identity as a result of the search rather than as a prerequisite for it. This makes facesearching uniquely suited to situations where you have a face but no other information. For a comparison with another dedicated face search tool, read our facesearching vs FaceCheck ID analysis.
Feature Comparison
The table below summarizes how facesearching and Pipl compare across the dimensions that matter most for people search.
| Feature | facesearching | Pipl |
|---|---|---|
| Search input | A photo (face) | Name, email, phone, or username |
| Underlying technology | Biometric facial recognition | Identity resolution and data aggregation |
| Works with photo only, no name | Yes, core capability | No, requires text identifiers |
| Public records and contact data | Limited | Yes, a core strength |
| Social media profile discovery | Yes, by face match | Yes, by identity linkage |
| News and video matches | Yes | Limited |
| Confidence scores | Yes, per match | Match strength indicators |
| Photo handling | Deleted immediately after search | No photo upload required |
| Pricing | Pay-as-you-go from $2 | Subscription and enterprise plans |
Accuracy and Data Sources
Accuracy means different things for each tool because they draw from different sources. Pipl excels at compiling records-based data — addresses, associated phone numbers, known relatives, and historical identifiers — and linking them to a name. Its accuracy depends on the quality and recency of the underlying records and data broker feeds. facesearching excels at visual identity — confirming that the face in your photo matches faces found on social media, in news articles, or in video content. Its accuracy depends on image quality and the breadth of the indexed web. The two are highly complementary: Pipl tells you what is known about a name, while facesearching tells you whether a face is connected to that name across the web. For more on the technology behind face matching, see our complete guide to facial recognition.
Privacy and Data Handling
Privacy considerations differ significantly. Pipl aggregates and resells data that already exists in public records and data broker ecosystems, which raises questions about data brokerage and the right to be forgotten. facesearching indexes only publicly available web content and deletes your uploaded photo immediately after each search completes, so it does not build a persistent database of your search activity. Neither tool accesses private accounts or non-public records, but their data models are fundamentally different — records aggregation versus public web face indexing. For a broader privacy discussion, read our face search privacy FAQ.
Pipl answers what do the records say about this name? facesearching answers who does this face belong to? Together they form a complete identity picture that neither can build alone.
When to Choose Pipl
Pipl is the right choice when you already have a verified name and at least one corroborating identifier, and you need to compile records-based data such as addresses, associated contacts, relatives, and historical data. It is particularly strong for enterprise due diligence, fraud investigations that begin with a known subject, and skip tracing where you need to locate someone whose name you already have.
When to Choose facesearching
Choose facesearching when you have a photo and little or no identifying information. If you met someone online and only have their picture, if you found a suspicious image and want to know if it is stolen, or if you want to verify that a face appears under a consistent identity across the web, facesearching is the tool built for that exact scenario. The ability to start from a single photo makes it indispensable for identity verification, catfishing detection, and finding someone when a name is unavailable or unreliable. When you are ready to begin, you can start a free face search on the facesearching home page.