Abstract
Home entrance is a vital entry point that should be secured at all times. A bi-level home access system was designed and developed using face authentication and hand gesture recognition. The system’s mainframe runs on a Raspberry Pi 3 minicomputer. The board serves as the computing platform to process various deep learning algorithms for face authentication and hand gesture recognition. It also serves as a communication hub which allows registered users to communicate with the system remotely via mobile application. Home occupants may also register emergency contacts such as their neighbours’ for quick response at their property. An Android mobile application was developed for remote user interface. Google Firebase platform was used to store user profile and historical data. The face authentication consists of two steps, namely face detection and face recognition. The Multitask Cascaded Convolutional Neural Network (MTCNN) was employed for face detection, while the Inception ResNet was used for face recognition. Upon successful face authentication, the system proceeds to read the user’s hand gesture. First, the system detects the hand using Single Shot MultiBox Detector (SSD) that runs on a Convolutional Neural Network (CNN). Next, a sequence of hand pose is recognised using the conventional CNN method. Based on experiments, the average detection/recognition accuracy under normal operating conditions using real face and realvideo captured by the system is approximately 95.7%. An occupant needs approximately 7s to complete the process to enter the house.
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Malaysia 2018 Crime & Safety Report, https://www.osac.gov/ , last accessed 2019/04/15.
Malaysian Police Reveals That On Average, 4 Children Go Missing Every Day in Our Country, https://www.worldofbuzz.com/, last accessed 2019/04/15.
Divya, R. S., Mathew, M.: Survey on various door lock access control mechanisms. In: 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1-3. IEEE, Kollam (2017).
Hung, C., Bai, Y., Ren, J.: Design and implementation of a door lock control based on a near field communication of a smartphone, In: 2015 IEEE International Conference on Consumer Electronics, pp. 45-46. IEEE, Taipei (2015).
Islam, K. T., Raj, R. G., Al-Murad, A.: Performance of SVM, CNN, and ANN with BoW, HOG, and image pixels in face recognition. In: 2nd International Conference on Electrical & Electronic Engineering, pp. 1-4. IEEE, Rajshahi, (2017).
Passarella, R., Fadli, M., Sutarno: Hand gesture recognition as password to open the door with camera and convexity defect method. In: 1st International Conference on Computer Science and Engineering, pp. 63-73. ICON-CSE, Palembang (2014).
Mesbahi, S. C., Mahraz, M. A., Riffi, J., Tairi, H.: Hand gesture recognition based on convexity approach and background subtraction. In: International Conference on Intelligent Systems and Computer Vision, pp. 1-5. IEEE, Fez (2018).
Tasnuva, A.: A neural network based real time hand gesture recognition system. International Journal of Computer Applications 59(4), 17-22 (2012).
Nagi, J., Ducatelle, F., Di Caro, G. A., Cireşan, D., Meier, U., Giusti, A., Nagi, F.: Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: IEEE International Conference on Signal and Image Processing Applications, pp. 342-347. IEEE, Kuala Lumpur (2011).
Abbas, Q., Ibrahim, M. E.A., Jafar, M.A. A comprehensive review of recent advances on deep vision systems. Artificial Intelligence Review 52(1). 39-76 (2019).
Aleluya, E. R. and T. Vicente, C. : Faceture ID: face and hand gesture multi-factor authentication using deep learning. Procedia Computer Science 135, 147-154 (2018).
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters 23(10), 1499-1503 (2016).
Szegedy, C., Ioffe, S., Vincent. V.: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. In: 31st AAAI Conference on Artificial Intelligence, pp. 4278-4284. AAAI, San Francisco (2017).
Victor Dibia, Real-time Hand-Detection using Neural Networks (SSD) on Tensorflow, (2017), GitHub repository, https://github.com/victordibia/handtracking, last accessed 2019/03/30.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., Berg, A. C.: SSD: Single shot multibox detector. In: European Conference on Computer Vision, pp 21-37. Springer-LNCS, Amsterdam (2016).
LeCun, Y., Kavukcuoglu, K. and Farabet, C.: Convolutional networks and applications in vision. In: IEEE International Symposium on Circuits and Systems, Paris, pp. 253-256. IEEE, Paris (2010).
Keras Tutorial: The Ultimate Beginner’s Guide to Deep Learning in Python, https://elitedatascience.com/keras-tutorial-deep-learning-in-python, last accessed 2018/11/05.
Labeled Faces in Wild Home, http://vis-www.cs.umass.edu/lfw/, last accessed 2018/11/30.
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Tham, K.Y., Cheam, T.W., Wong, H.L., Fauzi, M.F.A. (2020). Development of a Bi-level Web Connected Home Access System using Multi-Deep Learning Neural Networks. In: Alfred, R., Lim, Y., Haviluddin, H., On, C. (eds) Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 603. Springer, Singapore. https://doi.org/10.1007/978-981-15-0058-9_22
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DOI: https://doi.org/10.1007/978-981-15-0058-9_22
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