How to Use Face Recognition in App Development Using Deep Learning

Facial recognition software represents an exciting time in the world of app development.

If you are building an app and plan on using facial recognition technology, you need to get your head around the concept of deep learning.

Deep learning involves extracting face embeddings from facial images. Incidentally, these embeddings will be unique to individual faces. Without a doubt, the best way to perform this task is to train a deep neural network.

There are two types of deep neural networks:

Pre-Trained Models

You can find many pre-trained neural network models including DeepFace, FaceNet, and many others. This method takes a lot less time because the models already come equipped with face recognition algorithms.

Develop the Network From Scratch

Developing a neural network from the ground up is much more difficult, requiring million of images compared to the thousands required for pre-train networks. This takes much more effort but is perfect for complex face recognition systems with multiple functions.

Why Use Facial Recognition?

There are numerous reasons why you may use facial recognition software in a mobile app. As technology continues to develop, new business ideas will inevitably crop up. Currently, face recognition is used in the following ways:

#1. System Security

One of the most popular uses of facial recognition is security authentication. A few years ago, Amazon invented their or method of authentication called “Image Analysis for User Authentication.” This software lets users make certain transactions by performing a particular action with their face (a nod or smile, for example). When the camera detects said action, their identity is confirmed, and the transaction is processed.

#2. User Engagement

One way cafes, hotels, and similar establishments increase loyalty with their customers is through facial recognition. For example, many fast-food restaurants are using facial recognition to let customers order through their mobile apps. When they collect their order, they are greeted in person. As such, one could argue that facial recognition allows for an added personal touch in many instances.

#3. Safety

Facial recognition is also being used to keep people safe. For example, construction company Caterpillar has used facial recognition to protect drivers from falling asleep at the wheel. Thanks to an innovative piece of tech, they analyze the driver’s eye and head position when they are at the wheel. If they drop below a certain level, the system contacts headquarters to wake up the driver.

Other Examples of Deep Learning in Software Development

Interestingly, face recognition is not the only task that benefits from deep learning software. These include:

#1. Masked face recognition

Due to COVID, facial recognition technology has had to adapt to people wearing face masks. Deep learning algorithms have led to cameras recognizing faces covered by masks. Engineers have has to utilize numerous algorithms like periocular recognition and face-eye-based multi-granularity to enhance facial recognition software. As such, cameras can now recognize masked faces up to 95% of the time, just from features like the face contour, ocular details, eyes, cheekbones, etc.

#2. Detect Defects

In recent times, many manufacturers have implemented AI-centric visual inspection software to look for defects in their products. Deep learning algorithms can help find small cracks and scratches that may have been overlooked by a human.

#3. Detect Body Abnormalities

The Israeli company Aidoc has used deep learning in the field of radiology. This technology analyzes medical images to detect physical abnormalities in various body parts, including the chest, abdomen, and head.

#4. Emotion recognition

Nowadays, it is entirely possible to recognize human emotions through deep learning. These algorithms can identify specific points of the human face and tell the difference between a neutral facial expression and a positive or negative one.

As you can see, the uses of deep learning expand far beyond that of simple facial recognition. So, while the technology may be challenging to grasp, it presents many exciting possibilities for app development moving forward.

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