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Simplifying Identity Verification With Face Recognition Deep Learning 


Technological advancements in computing power brought a digital revolution in the world during the last few decades. Artificial intelligence (AI) and machine learning (ML) technologies presented a new feature, face recognition between 1964 and 1966 when programming computers were experimented to recognize human faces. By the end of 1980s, face recognition had established its worth and became the need of businesses for identity verification processes. Now face recognition deep learning has become a popular technique for identity verification and 

fraud detection in today’s digital landscape. This blog will explore face recognition deep learning and how it works for the identification of individuals to avoid spoofing and other types of fraud.

Face Recognition: A Quick Review

Face recognition is an AI technology that is used to recognize faces in an image, video, or in real-time. Being a category of biometric security it is also called biometric face recognition. There are two techniques that are used for face recognition:

  1. Face Recognition Using Machine Learning

In this method, Support Vector Machines (SVM) are used for face recognition. These machines find the boundary between different classes and by using machine learning methods.

Applications of Face Recognition Deep Learning

  1. Face Recognition Using Deep Learning

In deep learning data is processed through multiple layers of arrays by a special kind of neural network that is called convolutional neural network (CNN). The elementary units of this network are convolutional layers that are composed of several filters which have the ability to detect the special features of an image. The former layers focus on the broader features and the subsequent layers focus on the special features. Deep learning solves the problem of facial change detection and improves the accuracy of facial recognition. This technique is suitable for face recognition software programs and applications. 

Application of Facial Recognition Deep Learning in different industries

Digitization has increased the demand for online transactions in different businesses especially in banks, financial institutions, and e-commerce companies. In these fields the demand for personal information is high which increases the chances of identity theft and other illegal activities by the imposters. In this regard, the verification of the identity of customers is necessary for the protection of businesses and customers both from spoofing, deep fakes and other cybercrime. The safety of personal information is yet another challenge. Here, identity verification of customers is essential for businesses not only for secure and smooth customer onboarding, but it is a part of regulatory compliance.

With the advancement of science and development in technology, the Identity verification process requires sophisticated facial recognition scanner to authenticate the identity of the customers.

Face Recognition Solution

A face recognition solution is a software that provides face recognition services. This automated system has the ability to catch a face from a capture or video and match it with the faces available in a given database. Hence, providing businesses an opportunity to verify the identity of their customers within a few minutes. It makes customer onboarding easier by simplifying the verification process. Face recognition solutions are used to increase the accuracy of the identification process and eliminate the chances of fraud and spoofing. 

Following are common steps that are followed by a face recognition solution to verify the identity of a user.

  1. Data Gathering

The user is asked to provide preliminary information e.g. name, address, and identity card number. 

  1. Uploading An Image

User is asked to upload a real-time selfie and an image of his identity document during the video call. 

  1. Authenticating Identity Document

Powerful algorithms scrutinize special features and written text on the document to confirm the authenticity of the document.

  1. Capturing Biometrics and Conducting A Liveness Check

Biometric face recognition technology creates a map reading the geometry of face features. The main elements that are considered include the shape of your nose, cheekbones, lips, and ears. The distance between your eyes, from chin to forehead is also read. Then this analog data is converted into digital form or a mathematical formula. This coded form is called a face print or signature and is saved at the back end. Biometrics facial recognition is integrated with face liveness verification that confirms that the user is real and live. This eliminates all kinds of deep fakes by detecting  significant changes, such as shadow displacement, skin color or texture change.

  1. Comparing ID Image With Selfie

Facial recognition deep learning technology is used to compare the selfie with the id image to confirm that selfie matches with the ID photo provided by the user.  Distortion and skin texture  analysis along with 3D sensory techniques are used to confirm the liveness of the user. 

  1. Verifying Identities

If the selfie matches the ID image according to the provided data the identity is verified.


Face recognition deep learning is an advanced technology that can make identity verification processes simpler, easier and accurate. A face recognition solution uses this biometric method to verify the identities of individuals and to protect businesses from spoofing and other cybercrime. It is a popular application that is used in phones for unlocking system, detecting criminals in security systems. Eliminating the need for passwords or physical presence, it makes facial biometric verification convenient for both businesses and customers.

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