COMPARATIVE STUDY OF MACHINE LEARNING AND DEEP LEARNING ALGORITHM FOR FACE RECOGNITION

Abstract

In the present world, biometric systems are used to analyze and verify a person's distinctive bodily or behavioral features for authentication or recognition. Till now, there are numerous authentication systems that use iris, fingerprint and face feature for identification and verification, where the face recognition-based systems are most widely preferred, as they do not require user help every time, are more automated and are easy to function. This review paper provides a comparative study between various face recognition techniques and their hybrid combinations. The most commonly used datasets in this domain are also analyzed and reviewed. We have also highlighted the future scope and challenges in this domain, as well as various Deep Learning (DL)-based algorithms for facial recognition.

Keywords

• face recognition
• local binary pattern
• convolutional neural networks
• principal component analysis
• histogram of oriented gradient