Face Recognition Based on Cascade Classifier Using Deep Learning
Praveen Kumar Mannepalli, Devendra Singh Kushwaha, Sanjay Kalamdhar, Vishakha Niraj Nagrale, Vikram Rajpoot · 2023
The human face is significant because it is used as a tool of identification in our daily lives. One kind of biometric identification is Face Recognition (FR), which works by memorizing and using a person's unique facial features to identify that person in the future. Numerous researchers have been interested in biometric facial recognition technology due to its widespread use. Because of the non-contact nature of the process, FR technology surpasses other biometric-based authentication methods like fingerprint, palm print, & iris recognition. One such potential use for facial recognition (FR) algorithms is in remote identification when no physical touch or interaction with the target individual is required. This study proposes a method for constructing an FR system with a learning strategy depending on the DCNN algorithm. The approach of Deep Learning (DL) is presented for learning & assessing all samples & new inputs in a scheme. The ORL and YALE face datasets are used in studies that compare the findings to those produced by the Deep CNN approach. The proposed DL method uses a systematic approach to learning from and assessing new inputs and samples. The proposed model Deep CNN obtained 96.04 percentage points (or a validation accuracy of 62.2%) over the ORL dataset after 100 iterations with a loss of 17.33 percent. DCNN achieves a 99.99% training accuracy and a 99.89% validation accuracy without any data loss after 100 epochs of training on the Extended Yale B dataset. The proposed DL method analyses and rates are existing data from the system and fresh face inputs. In addition, it gathers information from the facial features & enhances the D-CNN algorithm for facial classification. Utilizing CNN (Convolutional Neural Networks), the accomplishments in several contests are advancing and becoming the focus of study.