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Outline

IRJET- Personalized Smart Mirror with User Detection

2021, IRJET

Abstract

It is a growing need for one to organize one's day in an efficient manner. One of the ways is to use a smartphone which is proving to be more destructive than productive. In order to reduce screen time on a smartphone, we can use a daily use object, a mirror, to help organize the day while not having to reach out to your smartphone for basic needs. The mirror can be used as a smart device that can offer services like displaying weather, date and time, latest updates of news headlines, user personalized daily tasks and reminders, some motivational quotes to get the day going positively with voice assistance. This use of the mirror can help the user save time and multitask by planning their day. The smart device uses Raspberry Pi as the central controller to dictate the actions to perform. When a user stands in front of this smart mirror, the controller recognizes the person and it displays the tasks that a particular user has to perform. It can also be used to drop messages for other users registered under the same smart system. The mirror allows to command the mirror using voice and choose what is to be done, e.g., Display the user tasks or display messages. The smart device comes with a mobile application that enables other users to check their tasks or messages when the mirror is unreachable.

References (27)

  1. Building an iot magic mirror with hosted web apps and windows 10. https://blogs.windows.com/msedgedev/2016/05/31/ magic-mirrorhosted-web-app/.
  2. Ivette Garcia, Eduardo Salmon, Rosario Riega, and Alfredo Barrientos. Implementation and customization of a smart mirror through a facial recognition authentication and a personalized news recommendation algorithm. pages 35-39, 12 2017.
  3. Yong Sun, Liqing Geng, and Ke Dan. Design of smart mirror based on raspberry pi. pages 77-80, 01 2018.
  4. Venkataraman Chayapathy, Dr anitha g.s, and B Sharath. Iot based home automation by using personal assistant. pages 385-389, 08 2017.
  5. Sal Benk, Youssef Elmir, and Abdeslem Dennai. A study on automatic speech recognition. 10:77-85, 08 2019.
  6. Mohammad Moattar and Mahdi Homayoonpoor. A simple but efficient real-time voice activity detection algorithm. European Signal Processing Conference, 12 2010.
  7. Raju Nadaf, Rubina M, Sujata P, and Vasudha Bonal. Smart mirror using raspberry pi for human monitoring and intrusion detection. pages 116-121, 07 2019.
  8. S. Athira, F. Francis, R. Raphel, N. S. Sachin, S. Porinchu, and S. Francis. Smart mirror: A novel framework for interactive display. In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pages 1-6, 2016.
  9. Raspberrypi 4 tech specs. https://www.raspberrypi.org/products/raspberrypi-4- model-b/specifications/.
  10. Raspberry pi 4 model b. https://www.raspberrypi.org/products/raspberrypi-4- model-b/.
  11. Shuo Xu, Yan Li, and Wang Zheng. Bayesian multinomial na¨ıve bayes classifier to text classification. pages 347- 352, 05 2017.
  12. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander Berg. Ssd: Single shot multibox detector. volume 9905, pages 21-37, 10 2016.
  13. Face detection -opencv, dlib and deep learning ( c++ / python). https://www.learnopencv.com/face-detection-opencv- dlib-and-deeplearning-c-python/.
  14. Wang Yang and Zheng Jiachun. Real-time face detection based on yolo. pages 221-224, 07 2018.
  15. Prabin J, P. Poornima, and Kukkapalli Kumar. A novel method for color face recognition using knn classifier. 02 2012.
  16. Guodong Guo, Stan Li, and Kapluk Chan. Face recognition by support vector machines. Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 02 1970.
  17. Musab Cos¸kun, Ays¸egul Uc¸ar, ¨ Ozal yıldırım, and Yakup Demir. Face ¨ recognition based on convolutional neural network. 11 2017.
  18. Nikhil Thakurdesai, Nikita Raut, and Anupam Tripathi. Face recognition using one-shot learning. International Journal of Computer Applications, 182:35-39, 10 2018.
  19. Neural networks -one shot learning. https://www.youtube.com/watch?v=r8LLorRACPM.
  20. Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. pages 815-823, 06 2015.
  21. Dattaraj Rao, Shruti Mittal, and S. Ritika. Siamese neural networks for one-shot detection of railway track switches. 12 2017.
  22. Ifeanyi Nwakanma, Ikenna Oluigbo, and Okpala Izunna. Text -to -speech synthesis (tts). International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 154-163, 2:154-163, 03 2014.
  23. Prerana Das, Kakali Acharjee, Pranab Das, and Vijay Prasad. Voice recognition system: Speech-to-text. Journal of Applied and Fundamental Sciences, 1:2395- 5562, 11 2015.
  24. The ultimate guide to speech recognition with python. https://realpython.com/python-speech-recognition/.
  25. Muhammad Firmansyah, Anand Paul, Deblina Bhattacharya, and Gul Urfa. A.i. based embedded speech to text using deepspeech, 02 2020.
  26. Veton Kepuska and T.B. Klein. A novel wake-up-word speech recogni-¨ tion system, wake-up-word recognition task, technology and evaluation. Nonlinear Analysis, 71:2772-2789, 12 2009.
  27. Shi-Xiong Zhang, Zhuo Chen, Yong Zhao, Jinyu Li, and Yifan Gong. End-to-end attention based text-dependent speaker verification. 01 2017