Multi-Module Convolutional Neural Network Based Optimal Face Recognition with Minibatch Optimization

Technology is getting smarter day by day and facilitating every part of human life from automatic alarming, automatic temperature, and personalised choice prediction and behaviour recognition.Such technological advancements are using different machine learning techniques for artificial intelligence.Face recognition is also one of the techniques to develop futuristic artificial intelligence-based technology used to get devices equipped with personalised features and security.Face recognition is also used for keeping information of facial data of employees of any company citizens of any country to get tracked and control over crimes in unfair incidents.For making face recognition more reliable and faster, several techniques are evolving every day.One of the fastest and most dependable face recognitions is CNN based face recognition.This work is designed based on the multiple convolutional module-based CNN equipped with batch normalisation and linear rectified unit for normalising and optimising features with minibatch.Faces in CNN's fully connected layer are classified using the SoftMax classifier.The ORL and Yale face datasets are used for training.The average accuracy achieved is 94.74% for ORL and 96.60% for Yale Datasets.The convolutional neural network training was done for different training percentages, e.g., 66%, 67%, 68%, 69%, 70%, and 80%.The experimental outcomes exhibited that the defined approach had enhanced the face recognition performance.