Use Face Recognition with Deep Learning in App Development

This approach consists of interaction with devices through one of the common standard automated interfaces such as USB, DVI, HDMI, SATA, etc. In such cases, there is no need for complex configuration of each of the devices and the development of additional integration solutions. We have already touched on the topic of what factors affect the accuracy of facial recognition.

  • The improvement of DL methods occurs, including, through the use of an increasing number of layers of neurons that are in networks.
  • We construct the inputs array where we are going to assign the imageUrl that we get from the frontend input.
  • This section describes key steps for setting up your development environment
    specifically to use Face Detector.
  • He developed a system that classified photos through a RAND tablet, a graphical computer input device.
  • PostgreSQL provides robust support for advanced data types, indexing, and transactions, making it suitable for complex applications with high data volume and concurrency requirements.
  • We will consider in which cases ready-made solutions will suffice and when it is necessary to create custom face recognition software from scratch.

This section describes key steps for setting up your development environment and
code projects specifically to use Face Detector. For general information on
setting up your development environment for using MediaPipe tasks, including
platform version requirements, see the
Setup guide for Android. The code sample described in these instructions is available
GitHub. For more information about the capabilities, models, and
configuration options of this task, see the Overview. Use one of the Face Detector createFrom…() functions to
prepare the task for running inferences.


It is a very effective biometric recognition tool in the current COVID-19 scenario because it is a more robust authentication system and contactless. However, a machine has to be trained step by step, and this is done most effectively using deep learning technologies. A situation where you consider facial biometric identification as a step in a more complex process is possible. For example, you want to verify the user of your software in this way to provide him with a personalized offer or access to confidential information.

face detection app dev

Some of the most popular ones include Microsoft computer vision, OpenCV, Kairos, Inferdo, Face++, etc. Woodrow Wilson Bledsoe, the father of facial recognition implemented facial recognition manually in 1960. He recorded the coordinates of facial features like eyes, nose, mouth, hair line, etc. manually. Once unsuspended, codesphere will be able to comment and publish posts again.

Face detection

The four main steps of facial recognition are face detection, data normalization/alignment, feature extraction, and recognition. The deep learning algorithms train the systems in how to localize faces, extract features, compare images and finally identify the faces. These algorithms are designed like an animal’s neural network and are called Artificial Neural Networks (ANN). Two types of ANNs – convolutional neural networks and deep auto-encoder networks – are the most popular algorithms for training facial recognition systems.

face detection app dev

Captured images and their corresponding records are managed using an Admin Panel that acts as a portal to a database that contains stored photos and IDs. Both the Admin Panel and database are prepared and entered into the biometric identification system before its implementation. Still, unidentified images and IDs can be added to the existing database using the Admin Portal. For face detection we experimented with several processes and discovered that Caffe Face tracking and TensorFlow object detection models provided the best detection outcomes. A camera application triggers the detection and recognition workflow.

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