> For the complete documentation index, see [llms.txt](https://docs.kby-ai.com/help/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.kby-ai.com/help/demo-projects/server-windows-linux/1-n-facerecognition-face-search-docker.md).

# 1:N FaceRecognition(Face Search) - Docker

## Overview

This project demonstrates an advanced `1:N face recognition` technology implemented via a `Dockerized Flask API`.

This demo performs `1:N face recognition`, `face search SDK` derived from `KBY-AI`'s [face recognition server SDK](https://hub.docker.com/r/kbyai/face-recognition) by implementing the functionalities to register face and search face from database(`PostgreSQL`).\
This demo offers `API`s to enroll face, to search face, to see database, to clear database. And every `API` can be customized by updating [app.py](https://github.com/kby-ai/FaceSearch-Docker/blob/main/app.py) file accordingly.

## SDK

{% content-ref url="/pages/of2yT7uMutmqK03HhwNQ" %}
[Face Search SDK(1:N Face Recognition) - Server](/help/product/face-liveness-detection-sdk-face-recognition-sdk/face-search-sdk-1-n-face-recognition-server.md)
{% endcontent-ref %}

## Github

{% embed url="<https://github.com/kby-ai/FaceSearch-Docker>" %}

## Test Online

{% embed url="<https://web.kby-ai.com/>" %}

## Postman Endpoints

To test the `API`, you can use `Postman`. Here are the [endpoints](https://github.com/kby-ai/FaceSearch-Docker/blob/main/kby-ai-facesearch.postman_collection.json) for testing:

1. `http://<your-base-url>/register`\
   This `API` enrolls face data from image base64 format and save it to database(`PostgreSQL`)<br>
2. `http://<your-base-url>/search`\
   This `API` seeks face similar to input face among database and returns enrolled image ID and similarity score.<br>
3. `http://<your-base-url>/user_list`\
   This `API` shows all data enrolled on database(`PostgreSQL`).<br>
4. `http://<your-base-url>/remove_all`\
   This `API` removes all data from database.

<figure><img src="/files/VW9WpyE0cPtfGxRNRshn" alt=""><figcaption><p>Example of face search endpoint</p></figcaption></figure>

## How to Run

### 1. System Requirements

**CPU:** `2 cores` or more (Recommended: `8 cores`)&#x20;

**RAM:** `4 GB` or more (Recommended: `8 GB`)&#x20;

**HDD:** 4 GB or more (Recommended: `8 GB`)&#x20;

**OS:** Ubuntu 20.04 or later&#x20;

**Dependency:** OpenVINO™ Runtime (Version: `2022.3`)

### 2. Setup and Test

* Clone the project:

```bash
git clone https://github.com/kby-ai/FaceSearch-Docker.git
```

* Download the model from `Google Drive`: [click here](https://drive.google.com/file/d/1ExXnc-QMVCFtGoP3xOkjoQFq56hO0PV0/view?usp=sharing)

```bash
cd FaceSearch-Docker

wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=19vA7ZOlo19BcW8v4iCoCGahUEbgKCo48' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=19vA7ZOlo19BcW8v4iCoCGahUEbgKCo48" -O data.zip && rm -rf /tmp/cookies.txt

unzip data.zip
```

* Build the `Docker` image:

```bash
sudo docker build --pull --rm -f Dockerfile -t kby-ai-face-search:latest .
```

* Get `Machine Code`:

```bash
sudo docker run -e LICENSE="xxxxx" kby-ai-face-search:latest
```

* Send us the `machine code` obtained.

<figure><img src="/files/GxxsR5xRJe0Gw5imeRpa" alt=""><figcaption><p>How to get machine code</p></figcaption></figure>

* Update the `license.txt` file by overwriting the license key that you received from `KBY-AI` team.
* Run the `Docker` container:

```bash
sudo docker run -v ./license.txt:/root/kby-ai-face/license.txt -p 8081:8080 -p 9001:9000 kby-ai-face-search:latest
```

<figure><img src="/files/uCJdh9tfHzDE41ZgZgVt" alt=""><figcaption><p>Activation value 0 indicates that SDK has been activated successfully.</p></figcaption></figure>