https://jjcit.org/paper/142 COMPARATIVE STUDY OF MACHINE LEARNING AND DEEP LEARNING ALGORITHM FOR FACE RECOGNITION 10.5455/jjcit.71-1624859356 Nikita Singhal,Vaishali Ganganwar,Menka Yadav,Asha Chauhan,Mahender Jakhar,Kareena Sharma Face recognition,Local binary pattern,Convolutional neural networks,Principal component analysis,Histogram of oriented gradient 542 229 28-Jun.-2021 17-Aug.-2021 19-Aug.-2021 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.