With the development of the information society, the importance of identifying individuals has rapidly grown. In order to permit access to a secure electronic system, biometric authentication verifies the identity of a user based on the unique biological characteristics of the user. Examples of the unique biological characteristics include, but are not limited to, fingerprints, hand geometry, earlobe geometry, retina and iris patterns, voice waves, keystroke dynamics, DNA, facial features and signatures.
Face recognition uses a non-contact method to identify users based on their facial features and is thus, deemed more convenient and competitive as compared to the other biometric authentication methods.
Some of the areas where face recognition may be used are safety, security and surveillance, access control, smart home, augmented reality and image-based search engines. However, there are many factors that may affect the performance of a face recognition system. Examples of the factors include, but are not limited to, gender, age, race, facial expressions, face direction, size of the face, facial hair, jewelry, illumination conditions and environmental factors. Therefore, there is a need to develop systems for face recognition that are robust against these factors.
Furthermore, there is a growing need to perform face recognition on processors embedded in smart devices such as smart-cameras and/or wearable devices.
Although, there are various products and applications available in the market for face recognition, the existing solutions have issues related to accuracy and scalability. Therefore, there is a need for efficient and accurate ways for recognizing faces in images.