The [Face Analysis API](https://api4.ai/apis/face-analysis) is one of the market's most cost-efficient solutions, offering advanced face detection and comparison. It leverages deep learning technology to automate face analysis processes in pictures. The API detects human faces in images, provides coordinates of the detected faces, and includes a confidence score indicating detection accuracy. It automatically identifies five key points on a human face: the left and right eyes, nose, and the corners of both lips. Optionally, the algorithm returns an embedding for each detected face, allowing it to accurately determine if different faces belong to the same person. Developed by [API4AI](https://api4.ai), this API is built on a robust cloud technology stack, ensuring full operability, scalability, and stable uptime. Our mission is to deliver out-of-the-box, self-contained AI solutions that integrate seamlessly into any application with just a few simple steps. ## 👨‍🦲 Face and landmark detection ![](https://storage.googleapis.com/api4ai-static/rapidapi/face_analysis_1.png) The algorithm detects multiple faces and sets of landmarks within an image (where landmarks refer to key points identified on a face): * The face bounding box is represented by four floating-point numbers: the `x` and `y` coordinates of the top left corner, as well as the `width` and `height` of the box. These numbers are normalized relative to the image size. * Facial landmarks are represented by the `x` and `y` coordinates of five key points: the nose, the left and right eyes, and the left and right corners of the mouth. These numbers are also normalized relative to the image size. ## 🔍 Recognition ![](https://storage.googleapis.com/api4ai-static/rapidapi/face_analysis_2.png) This Face Detection API is also capable of providing "embeddings" for individual faces and recognition purposes. An embedding is essentially a compact representation of a face in the form of a vector of floating-point numbers. Embeddings are represented by vector of `512` float values. Depending on query parameters, embedding vector may be calculated for each face detected in an image or for the whole image (usefull for cases when an image consists of a single cropped face). ## 👉 Demos * 🤖 Telegram demo bot: https://t.me/a4a_face_analyzer_bot * 🔗 Web demo: [https://api4.ai/apis/face-analysis](https://api4.ai/apis/face-analysis?utm_source=face_analyzer_rapidapi&utm_medium=about&utm_campaign=rapidapi) ## 👉 Contacts * 📩 Email: hello@api4.ai * 💬 Telegram: https://t.me/a4a_support_bot * 🔗 Instagram: https://www.instagram.com/api4ai * 🔗 Twitter: https://twitter.com/api4ai * 🔗 Facebook: https://www.facebook.com/api4ai.solution