Convolutional Neural Network and Hidden Markov Model based Face Recognition
In this paper, the presentation of the proposed Convolutional Neural Network (CNN) with two surely understood picture acknowledgment strategies, for example, Principal Component Analysis (PCA), Hidden Markov Model(HMM). In our trials, the general acknowledgment precision of the PCA, LBPH, KNN (k nearest neighbor) and proposed CNN is illustrated. Every one of the investigations were executed on the ORL database and the acquired test results were appeared and assessed. This face database comprises of 400 distinct subjects (40 classes/10 pictures for each class). The test result demonstrates that the LBPH give preferable outcomes over PCA and KNN. These exploratory outcomes on the ORL database exhibited the viability of the proposed strategy for face acknowledgment. For proposed CNN we have gotten a best acknowledgment precision of 99.5 %. The proposed technique dependent on CNN the best in class strategies. Keywords - Face Recognition System, KNN, LBPH, Neural Networks, PCA.