Human Face Recognition System Based on Deep Learning


  • Sunesh Assistant Professor, Department of Information Technology, Maharaja Surajmal Institute of Technology, New Delhi, India
  • Anu Saini Assistant Professor, Department of Computer Science and Engineering, G.B. Pant Engineering College, New Delhi, India
  • Mamta Kumari Assistant Professor, Department of Computer Science and Engineering, Panipat Institute of Engineering and Technology, Panipat, India


Face recognition, Deep Learning, VGGFace2, OpenCV, Feature selection, Feature detection, Classification


For the past few decades, research on human face recognition has been ongoing. Face recognition has grown significantly in popularity as a result of its usage in the processing of biometric information. Its applicability is simpler and its working range is wider than that of other biometric technologies, such as fingerprint, iris scanning, signature, etc. Facial unlocking for mobile devices, criminal identification, and an automatic attendance system are just a few of the many uses for face recognition technology. Due to different face occlusions, different lighting conditions, shifting expressions and the effects of age, face recognition often becomes difficult. To deal these issues, a Deep learning-based face recognition approach has been presented in this paper. The proposed method operates in two phases, face detection and face identification, to provide solution to these issues. Deep learning and OpenCV are used in our suggested VGGFace2 model. Due to its great accuracy, deep learning is an effective and suitable way to do face recognition. To show the efficiency of our suggested facial recognition system, the outcome of experiments has been compared with existing papers.


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How to Cite

Sunesh, A. Saini, and M. Kumari, “Human Face Recognition System Based on Deep Learning”, IJRESM, vol. 5, no. 11, pp. 168–173, Nov. 2022, Accessed: Jun. 17, 2024. [Online]. Available: