Facial recognition system is a technology which can be used to verify or identify a person from a photograph or a video frame. There are multiple ways of facial recognition but the most commonly used one is by comparing some selected facial features and comparing it with those faces in the database. Also facial recognition is preferred over any other bio metric technologies such as iris recognition, finger print scanning, voice recognition and skin texture recognition because of its easy deployment and non-contact process.


The software tools for facial recognition can be divided into the following types:


2D Face Recognition

2D face Recognition has three methods. The first method exploits an isotropic smoothing, combined Gabor features and Linear Discriminant Analysis (LDA). The second approach is based on subject-specific face verification via Shape-Driven Gabor Jets (SDGJ), while the third combines Scale Invariant Feature Transform (SIFT) descriptors with graph matching.


3D Face Recognition

In 3D face recognition 3D geometry of the human face is used. 3D face recognition has the potential to achieve better accuracy than its 2D counterpart by measuring geometry of rigid features of the face. The 3D facial recognition helps in avoiding the shortcomings in the 2D face recognition algorithm such as, head orientation, make-up, different facial expressions and change in lighting.


Thermal Face Recognition

Thermal face recognition deals with the face recognition system that takes thermal face as an input. Thermal human face images are produced due to the body heat patterns of the human being. Thermal Infra –Red (IR) imagery is not affected by the ambient lighting conditions, as the thermal IR sensors can only take in the heat pattern emitted by the object. The range of human face and body temperature are almost the same and uniform, it varies from 35.5°C to 37.5°C which provides a consistent thermal signature. The thermal patterns of faces are derived primarily from the pattern of superficial blood vessels under the skin. The vein and tissue structure of the face is unique for each person and, therefore, the IR images are unique as well.



·         In online banking, the face recognition system will allow only the authorized person to access the bank account through the web services of the bank. There is a possibility for the customer to use the ATMs, mobile application, and other banking services using biometric identification to enhance the security during transactions.


·         Real-time emotion detection is a valuable application of face recognition in healthcare. It can be used to detect several emotions patients exhibit during their stay in the facility and analyze the data so as to determine how they are feeling. The results of the analysis may help identify where the patients need more attention in case they’re in pain or sad.

·         In order to attract a wider user base amongst the stiff competition from different applications, Social Media platforms have adopted facial recognition capabilities to diversify their functionalities.


·         In ID Management, issuing agencies need to prevent applicants from obtaining ID documents for a second time. Face recognition system allow agencies to find duplicate faces in multi-million photo databases within seconds.


·         In Border Control, Facial recognition in combination with ePassorts ensures secure and fast checking of travel documents and biometric verification of the passport holder's identity.

The 3D facial recognition technology is a more safe biometric system as it has a high scope to  recognize, and authenticate the facial characteristics of individuals with better accuracy.



With all the opportunities, there are also some controversies associated with this technology.

·         Civil rights organizations and privacy campaigners expressed their concern that privacy is being compromised by the use of Face recognition system. Some are scared that this system could lead to a “total surveillance society”.


·         Another controversy in association with the face recognition system is Facebook's Deep Face, with the claims alleging that Facebook is collecting and storing face recognition data of its users without obtaining informed consent, in direct violation of the Biometric Information Privacy Act.


·         Also, all over the world, law enforcement agencies have begun to use facial recognition software as an aid in the identification of criminals. Overall accuracy rates for identifying men (91.9%) are higher than for women (79.4%), and none of the systems accommodated a non-binary understanding of the gender. So the controversy here is that, face recognition is an imperfect technology in the law enforcement.



Growing concern towards secured transaction in healthcare, banking, retail, and government industries are increasing the adoption of advanced biometrics system such as face recognition system. There are companies who are offering customized face recognition solution for specific industries. For instance, Amazon has its own cloud-based face recognition service named “Rekognition” for law enforcement agencies. The solution is able to recognize as many as 100 people in a single image and can perform face match against databases containing tens of millions of faces. Growing smart phone implementation and increasing instances of identity threats is a boost to the use of face recognition system in these mobile devices. The widely availability of face recognition system on smartphone may be used as a biometric system during various business transactions.  However, misinterpretation of faces or emotions and high initial costs causes hindrance to the use of this technology. There are multiple controversies associated with the system. However, with advancement of face recognition technology, these limitation may likely to be overcome.


The identification and authentication solutions will borrow from all the aspect of biometrics in the future. This leads to “Biometrix” or “Biometric Mix” which is capable of guaranteeing total security for all the stakeholders. Similarly, deployment of deep learning in facial recognition system can create a technological disruption in the future. Deep learning is a central component of the latest-generation algorithms and holds the face detection, face tracking and face match as well as real-time translation of conversations.