{"data":{"markdownRemark":{"id":"17a0958e-87fb-568c-91ab-17183a611644","html":"","fields":{"slug":"/blog/face-validator-open-source-symfony3-facial-recognition-bundle/"},"frontmatter":{"title":"Face Validator: open source Symfony3 facial recognition bundle","cover":"/img/facevailidator.jpg","tags":["face recognition API","php"],"lead":"Have you ever used an application to upload a profile picture? Sure you have. That picture then shows up with every reference to the user. In the systems we create, this function is a frequent requirement and as such, we’re used to it. However, recently we’ve been working on an app with an added requirement: **the system must verify whether the photo sent by the user really contains a human face.** What else is important?","content":[{"body":"The system should also check if the face is sufficiently exposed, if the photo isn’t too blurred, and that the person in the photo isn’t wearing sunglasses. As a result, we’ve created **Face Validator**, an **open source Symfony3 facial recognition bundle**.\n\n\n\n## **Research**\n\nOur project team hadn’t dealt with the issue of facial recognition before so we decided to approach this task methodically, starting with an analysis of possible solutions. First, we looked into the availability of readymade **facial recognition solutions** we could implement in our system at a relatively low cost. We came across a few that could potentially meet our expectations:\n\n* Lambda Labs Face Recognition API\n* OpenFace\n* Google Vision API\n* SkyBiometry\n* Amazon Rekognition\n* MS Azure Face API\n\nWe tested all of them in order to select the best one, taking into consideration various factors, including reliability, speed, and accuracy, as well as the costs of implementation and use.\n\n## Lambda Labs Face Recognition API\n\n![Lambda Labs face recognition API](/img/lambda-labs-face-validator-article.png \"Lambda Labs offers face recognition API and album algorithm training. \")\n\nLambda Labs REST API allows, among other things, facial recognition in a photo sent as a URL to public storage. It also offers face recognition and album algorithm training (using collections of tagged pictures). When it comes to detection, you receive the coordinates of several points on the face and its size, as well as whether or not the person is smiling. Unfortunately, it provides no data on the level of blurring or face rotation and it doesn’t detect whether the face is covered by something.\n\nIn the free version, you get 1,000 face detection requests and 1,000 facial recognition requests.\n\nAfter analyzing and testing this solution, we came to the conclusion that the data provided by this API is not sufficient to create a complex face validator. Besides, the free trial – in comparison with other competitive solutions – doesn’t offer enough requests.\n\n## OpenFace\n\nAnother solution we considered was OpenFace. This is an open source library written in Python which provides a convenient API for photo pre-processing (preparing photos for use in neural networks), model training, and classification. It’s a really flexible tool which can be useful both in facial recognition and clustering. It contains ready mechanisms which make it easier for developers to use neural networks for these purposes. It also provides pre-trained models which can shorten the training process. Also, thanks to its use of the Torch framework, actions that require high computing power are transferred to the graphics card.\n\nOpenFace is certainly a great library which considerably simplifies the use of advanced tools for face classification, but facial detection and recognition itself is a problem of a slightly different nature and cannot be easily handled by this tool.\n\n