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Face Recognition is a very important research direction in computer vision and pattern recognition. Nowadays many scientists and engineers have been focusing on establishing accurate and efficient algorithms and methods for face recognition systems. In our day-to-day life, there are thousands of applications (Huang et al., 2011)[2], which are used for face recognition in today’s modern era.
The presented algorithms focus on the human face in a particular scene. For a human, it is easy to identify the faces of different people because they know how their faces look like, and of course, our brains are collecting data since birth. But for machines, it is too much difficult task to identify and recognize the face. The difficulties arise for finding the face because of different challenging situations like illumination conditions, variations in different types of the pose, occlusion conditions, excessive facial expressions, low resolution of the image, changes in facial features due to aging and complexity of the models (Yan et al., 2009)[8]. The machine works based on instructions given by the user. In the face recognition system, we have to train the model to identify and recognize the face from the given digital image. There are many algorithms or methods are available to train the model for the face recognition system. We have attempted to present the survey based on important and recent algorithms of the face recognition system.
This paper presents survey on face recognition techniques: SVM (Support Vector Machine) (Adegun & Vadapalli, 2020; Cherifi et al., 2019; Dino & Abdulrazzaq, 2019; Ghazal & Abdullah, 2020; Shi et al., 2020), CNN (Convolutional Neural Network) (Agrawal & Mittal, 2019; Ben Fredj et al., 2020; Ilyas et al., 2019; Jaiswal & Nandi, 2019; Ravi & Yadhukrishna, 2020), Eigenface Approach (Gupta et al., 2019; Hengaju & Sharma, 2020; Machidon et al., 2019; Mulyono et al., 2019; Zafaruddin & Fadewar, 2018), Gabor Wavelet Transform (Al-Obaydy & Suandi, 2018; Phan et al., 2019; Qin et al., 2020; Rashid & Abdulqadir, 2020; Zou et al., 2020), PCA (Principle Component Analysis) (Alahmadi et al., 2019; Arora & Kumar, 2020; Hu & Cui, 2019; Peter et al., 2018; Zhao et al., 2020) and HMM (Hidden Markov Model) (Ansari et al., 2018; Azar & Seyedarabi, 2019; Kiani & Rezaeirad, 2019; Rahul, Kohli, & Agarwa, 2018; Rahul, Mamoria, Kohli et al, 2018).