• Madan Piskje
• Dipak Kurade
• Hrushikesh Khaladkar
• Vishal Kolekar
• Baliram Deshmukh

Ensuring the security of our lives and property is currently one of the biggest challenges facing Smart Lock systems. The use of bio-metric authentication of users attracts around the world due to their convenience and acceptance. Par- ticularly in offline settings where digital selfies and ID document facial photos are linked. In fact, comparisons of selfies with ids have also been used in some broader programs these days, such as automatic immigration control. The great difficulty of such a process lies in limiting the differences between comparative facial images given their different origins. We propose a novel architecture for cross-domain matching problem based on deep features extracted by two well-referenced Convolutional Neural Networks (CNN). The results obtained from the data collected, called Face Data, with more than 93 percent accuracy, indicate the strength of the proposed face-to-face comparison problem and its inclusion in real time door lock security systems.

Convolutional Neural Networks (CNN), Smart door, Liveness detection automatic immigration control, Digital selfies, Face-to-face comparison problem.

"SMART HOME USING FACE RECOGNITION AND LIVENESS DETECTION IN MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 11, page no.f329-f334, November-2022, Available :http://www.jetir.org/papers/JETIR2211542.pdf

"SMART HOME USING FACE RECOGNITION AND LIVENESS DETECTION IN MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 11, page no. ppf329-f334, November-2022, Available at : http://www.jetir.org/papers/JETIR2211542.pdf