How to Build a Facial Recognition System for Your App

Guide to Face Detection and Recognition Software Development

14 min read


Automated face recognition quickly moved from the category of interesting novelties to a must-have feature in various business applications. If you are looking for a way to integrate this technology into your app, this article will explain the sequence of steps needed to do so. 

Relying on MobiDev’s practical experience in AI app development, which the company has been cultivating since 2018, I will explain in which cases ready-made solutions will suffice and when it’s necessary to create custom face recognition software from scratch. You will then be able to tell which way is better for your business and what it takes to succeed with this technology. 


Today’s face recognition software based on deep learning is one of the most up-and-coming options for quick and accurate identification of a person. Such a comprehensive solution can comprise various components seamlessly integrated into a complete system designed to address specific business requirements. Here are the key elements:

  • Neural network architecture. Convolutional Neural Networks (CNNs) have proven themselves to be highly effective for image classification and recognition tasks. Therefore, it is quite reasonable that we recommend using them. It is expedient to build a neural network for face recognition software on generally accepted architectural solutions, such as ResNet or EfficientNet. However, we may also use some new solutions if they better meet the product requirements.
  • Hardware power. When developing face recognition software using deep neural networks, it is necessary not only to increase the accuracy of recognition, but also to reduce the response time. This is why we think GPUs, for example, are better than CPUs for deep learning facial recognition systems.
  • The database program to be used. The database stores information about users’ faces. We recommend using SQLite, as this database management system meets the necessary requirements for similar projects and needs minimal configuration effort compared to its counterparts.
  • The application programming interface (API) is for communication between the system components. It is necessary to decide whether to develop independently or to use existing APIs. Quite often, the simplest and fastest solution turns out to be completely sufficient.
  • Inference time range. We determine which values of the inference time will be considered optimal. This is weighty when applying the developed neural networks in real-time.

The choice of components for face recognition app development requires finding a balance, or even a trade-off, between the accuracy of the solution and the speed of the system. Therefore, the best idea is to start with a detailed analysis of your business needs and their synchronization with AI technology capabilities. My colleagues and I will be happy to help you with this task.

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Face Recognition Deep Learning Methods

The core element of face recognition software is undoubtedly the deep learning model. It contains algorithms for the main stages of the process.  These stages are:  

Face detection > Data alignment > Feature extraction >Face Identification

The system also provides automation of actions that should follow recognition. Algorithms for such actions are usually custom-developed outside of the deep learning model.

The following face recognition deep learning methods are most used in actual practice:

  • Work with pre-trained models. This method saves time and other resources. In addition, the correct setting of pre-trained models allows you to prevent bias and ensure the correct operation of the system. The training datasets for this type of model have thousands of images.
  • Development of an artificial neural network from scratch. This approach makes it possible to develop complex and advanced face recognition systems with full functionality, which may include unique features. However, such development is more time-consuming and expensive, as it requires the formation of a training data set from millions of images.

If you follow the second path, then the decision regarding loss function is essential. A loss function can be used to evaluate how well an algorithm models a data set. This is done by calculating the difference between the network’s predictions and the actual data.      

The choice between the two methods is made based on the specifics and level of complexity of a particular problem, as well as the time available for its solution. At the same time, it should be noted that in the long term, a facial recognition system with unique functions is quite promising. 


Let’s consider the sequence of operation of custom face recognition software. I will also briefly describe the main workflow stages.


First, we need to detect that the static images or videos being studied contain exactly one or more faces. The model we use has a built-in algorithm for recognizing such objects as faces. We also set the format and parameters in which the captured image should be compared with the available data. 

Having found a face among the depicted objects, the face recognition software cuts it to the size of the frame we set. Next, the system sends the image to the server in a form convenient for comparison.


The fact is that the normalizing of images that we mentioned is not the only face-processing operation that may be required. The face can be taken from different angles or in poor lighting. However, the user expects the system to recognize faces under all conditions. Accordingly, the software must normalize or align the data to a form that will be compared to images from the database. Thanks to this, it is possible to reduce the influence of the factors mentioned above.


We pre-install a set of the facial embeddings we mentioned above, which need to be identified and extracted from each recognized face. Most of the time, such sets consist of 128 unique features. Next, a neural network extracts such facial embeddings from face images.


In this step, the system algorithmically compares the unique facial features with the data available in the database using mathematical calculations. There is always the possibility of error. It is impossible to guarantee a one hundred percent coincidence of the embeddings of the recognized face with the data of the database. Therefore, when we develop facial recognition software, we must set the embeddings’ matching level. Thus, a face will be considered recognized if the distance between the allegedly identical points on the compared images does not exceed the set level.

Face recognition software for different purposes requires different levels of precision. Customers of face recognition app development usually make this choice. As a rule, the highest facial features matching limits are set for systems involved in defense, security, finance, etc. In many other cases, when the price of a possible error is not so significant, more lenient requirements for the coincidence of facial embeddings are established. 


One part of the software deals with the further actions of the system that depend on the recognition conclusion. Such an action may be a notification of a facial identification conclusion, which is forwarded to the user or the server, via a chatbot or other channel. According to the received message, further actions will be performed by other parts of the system or by a person, for example, an operator.

Facial Recognition System Workflow

A common task of face recognition software is face verification to confirm identity. The system must provide an unequivocal conclusion that the person who has been checked is exactly the person whose data is entered into the database. This identification is used in biometric authentication systems as a security tool for accessing applications, services, or databases. Accordingly, an example of a further action of the face recognition system is to grant access to a user whose identity is confirmed.

Follow the link to the case study below to see how we implemented face and voice recognition features into an enterprise verification-as-a-service solution for securing access to sensitive data.

Face & Voice Recognition and Authentication Solution

View the case


After we have considered the face recognition system design, we can touch on the format in which this feature will be offered to end users. This should be decided from the standpoint of not so much a technical perspective, as through the use of business thinking. If face recognition has some self-sufficient value for you and your customers, bringing benefits, then this functionality can be implemented in a separate application.

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 them with a personalized offer or access to confidential information. In this case, you should enrich the existing application with a valuable feature. In this case, you have to ensure compatibility and integration with other elements of your current application.

Custom vs Ready-Made Models

Here, it is appropriate to return to the use of ready-made and custom-made solutions in face recognition system design. We talked about the pre-trained models above. There is also always the option to develop your own custom model. However, you need to carefully evaluate your capabilities for this, as well as the feasibility of this step. How you implement a face recognition system may depend on this.

For the effectiveness of a face recognition system based on deep learning, the quality and relevance of training data is more crucial than the development of algorithms or neural network parameters. 

  • A high-quality dataset allows you to implement any algorithm and train a neural network effectively.
  • The training data should be as close as possible to the images the system will later recognize.
  • The amount of data required depends on the purpose and application environment of the face recognition software.
  • Minimal training data is needed for consistent lighting and controlled conditions. Larger datasets are required for systems operating under challenging conditions (low-quality video, variable weather, poor lighting).

Data is the main “raw material” for machine learning, and accessing the required amount of high-quality, relevant data is the primary challenge in developing a model from scratch. Large corporations often start collecting data long before launching projects to use it. When purchasing data, it’s essential to evaluate the relevance and variability of the dataset. In many cases, such data can be biased. For example, datasets that do not account for diverse skin tones and facial features should be avoided.

A high-quality, varied dataset should include:

  • Images of people from different races, genders, and age groups.
  • Images taken under various lighting conditions, seasons, and weather.

So, it is advisable to start a new development project for a deep learning model for face recognition only if there is a dataset suitable for such a task. In particular, it is worth thinking about the development and training of new algorithms, having access to significant volumes of visual information, for example, data from large video surveillance systems for several years, etc.

If you do not have the necessary training data, then you can proceed to the consideration of pre-trained models. AI engineers who work on your project are able to choose a ready-made model with the necessary characteristics to cover your business needs. 

At the same time, as we said above, the part of the software that manages the actions of the system after recognition is beyond the pre-trained model and is the subject of custom face recognition development.


When implementing a face recognition system, you should be ready to interact with many peripheral devices. Beyond a doubt, the deployment and integration of the system can be provided as one of the aspects of custom face recognition software development services, with the help of appropriate SDKs.

Along with that, it is expedient to keep in mind the possibility of using plug-and-play solutions. 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.

When creating a facial recognition app, you should imagine the conditions under which it will operate, and what the priorities of both you, as a customer, and future end users will be. This will help engineers offer suitable technological approaches.

In particular, the choice between deploying the core elements of the system, i.e. models and data, in the cloud or using Edge AI is important. This affects the reliability, flexibility, and security of the software operation. 

The face recognition system based on the cloud is a common option that has many pros. But there are also substantial cons. Among them is the risk of data leakage during their transmission, and the dependence of the operation of devices integrated into the system on access to the Internet.

According to the Edge AI approach, custom face recognition software is installed and operated directly on devices. At the same time, devices can be portable, even wearable, and literally be at the user’s hand.

Cloud-based vs Edge AI Face Recognition System Options

Of course, the technical requirements for such equipment and its cost are increasing, since the power of the equipment must be sufficient for fast work. In addition, Edge devices must have their own GPU to quickly process incoming images. The internal memory, in turn, must contain all the necessary data.

At the same time, there are many advantages of such a face recognition system design. Reliability is much higher. First of all, there is the possibility of autonomous operation without an Internet connection and even without a power grid (when equipped with a rechargeable battery). The absence of the need for constant information exchange with the cloud via the Internet results in faster system operation and a higher degree of data protection.


The capabilities of AI face recognition that we described above are finding new applications in various spheres of life. The ability of face recognition software to provide an acceptable result in difficult conditions is especially in demand. Let’s turn to interesting examples.


It is not always possible even for people to recognize a familiar person in a medical mask. However, thanks to deep learning algorithms and convolutional neural networks, automatic authentication systems can recognize faces with parts covered by masks with up to 95% accuracy. Face-eye-based multi-granularity algorithms and periocular recognition models are applied. As a result, the system correctly identifies a face only by mask-free features – forehead, face contour, ocular and periocular details, eyebrows, eyes, and cheekbones.

How to Identify a Human Face Wearing a Mask


Based on large volumes of medical images, a neural network trained by deep learning algorithms can notice the signs of diseases in people.


Facial expressions are no longer a “black box” for automated systems. In combination with face detection, this makes it possible to come to a person’s aid in time or make a targeted offer of goods or services.


We can all agree that allowing access to an office or residential building to a face-identified person is much more in line with the fast pace of modern life. It can be much more efficient when compared to the requirement to carry and look for a key or card at the right moment.


Our experts deeply investigate the possibilities of facial recognition technology and research ways to overcome its current challenges and limitations. One of them is improving recognition accuracy. Developing internal expertise allows us to solve complex business tasks in the most intelligent way, helping our clients confidently move toward their goals. 

Whether you want to build software recognition software from scratch or upgrade your existing solution, my colleagues and I are happy to help you through our AI product consulting services. This way we’ll help you build an outstanding product with user-centric UX to meet your business goals.

Let’s talk about your project! We are eager to build something cool together!

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