http://www.iaeme.com/IJARET/index.asp 555 editor@iaeme.com International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 10, October 2020, pp. 555-563, Article ID: IJARET_11_10_059 Available online at http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=11&IType=10 ISSN Print: 0976-6480 and ISSN Online: 0976-6499 DOI: 10.34218/IJARET.11.10.2020.059 © IAEME Publication Scopus Indexed DEVELOPMENT OF HUMAN MACHINE INTERFACE USING SMART MIRROR AND FACE RECOGNITION ALGORITHM Omkar Vaidya Assistant Professor, Electronics and Telecommunications Engineering, Sandip Institute of Technology & Research Centre, India Gitanjali Jain PG Scholar, Electronics and Telecommunications Engineering, Sandip Institute of Technology & Research Centre, India Sanjay Gandhe Professor, Electronics and Telecommunications Engineering, Sandip Institute of Technology & Research Centre, India Gayatri Phade Associate Professor, Electronics and Telecommunications Engineering, Sandip Institute of Technology & Research Centre, India ABSTRACT The Human Machine Interface (HMI) based framework portrays the structure and advancement of a modern smart mirror that speaks to an unassuming interface for the surrounding home condition. The mirror permits characteristic methods between association through which the inhabitants can control the family unit savvy machines and access customized administrations. The smart mirror is competent to exhibit by extending home mechanization framework that gives a blend of family machines and different tweaked data administrations. In this paper, the HMI is developed using smart mirror framework that comprises of data related to climate expectation, date and time, clock, news channels and clients. These data can be taken from internet browser and utilizing python which give programming property and work show. The ARM Processor is associated with the cloud and gathered information from web to show the data on reflects. For security reason, we have planned the mirror with the assistance of face framework. The convolutional neural network based face recognition is employed and experimental result showed that 100 % accuracy of facial recognition is achieved. Key words: ARM Processor, Convolutional Neural Network, Human Machine Interface, Raspberry Pi, Smart Mirror