Abstract
Face recognition is the essential security system, which is subjected to get more scrutiny in recent years especially in the field of research and also in industry. This study addresses the various approaches for recognizing face back on neural network by adopting convolutional neural network (CNN). The study has done on different techniques of face alignment, preprocessing techniques, and also in the size of the face images. This paper explains the computational analysis of face recognition system and emphasizes the accuracies and constraints of the images. The predominant face alignment approaches used are Dlib and constrained local model (CLM). For training, Tan-Triggs preprocessing technique is used in face image size of 96 × 96 and 64 × 64. The face recognition grand challenge (FRGC) dataset is used for the analysis, and it produced the accuracy of range from 90 to 98.30% on corresponding approaches.
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Sharma, S., Kalyanam, A., Shaik, S. (2019). Technical Analysis of CNN-Based Face Recognition System—A Study. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_49
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DOI: https://doi.org/10.1007/978-981-10-8055-5_49
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