Human face detection is an outstanding biometric system and also widely used in machine vision and pattern recognition, due to its good performance in a range of applications such as surveillance systems, legal, security, authentication, and smart cars. Recognition of the human face has always been faced with a variety of challenges, which often result in a reduction of facial recognition systems efficiency Therefore, to address these problems, we need to use sets of knowledge, techniques, and methods of different resources. Numerous biometric verification frameworks like Iris, Deoxyribonucleic Acid (DNA), Vein, Finger Print endures the issues of data acquisition. Face Recognition (FR) plays very important role in biometric systems. The recognition rate on the face is primarily dependent on the selection of attributes. This study investigates the FR techniques. Most current techniques are widely described in five stages, face image acquisition, preprocessing, feature extraction, classification, and attribute recognition. According to the available literature work, real-time Face Recognition biometrics needs still better performance, resistance to being spoofing attack, and needs better recognition accuracy. Recognition accuracy can be enhanced by advanced techniques such as neural networks using feature extraction algorithms. In this paper, the methods of facial recognition and work done by researchers have been collected and the challenges in this field have been investigated to pave the way for researchers and future research.